import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import random
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
from matplotlib import pyplot as plt

# Specify the path to the main directory
main_directory = '105_classes_pins_dataset'

# Initialize counts
folder_count = 0
total_file_count = 0

# Iterate through the main directory
for root, dirs, files in os.walk(main_directory):
    # Count folders
    folder_count += len(dirs)
    
    # Count files in each folder
    folder_file_count = len(files)
    total_file_count += folder_file_count
    
    # Print the folder name and the number of files in that folder
    print(f"Folder: {os.path.basename(root)}, Files: {folder_file_count}")

# Print the overall results
print(f"\nNumber of folders: {folder_count}")
print(f"Total number of files: {total_file_count}")

Folder: 105_classes_pins_dataset, Files: 0
Folder: pins_Adriana Lima, Files: 213
Folder: pins_Alex Lawther, Files: 152
Folder: pins_Alexandra Daddario, Files: 225
Folder: pins_Alvaro Morte, Files: 139
Folder: pins_alycia dabnem carey, Files: 211
Folder: pins_Amanda Crew, Files: 117
Folder: pins_amber heard, Files: 218
Folder: pins_Andy Samberg, Files: 196
Folder: pins_Anne Hathaway, Files: 203
Folder: pins_Anthony Mackie, Files: 124
Folder: pins_Avril Lavigne, Files: 162
Folder: pins_barack obama, Files: 119
Folder: pins_barbara palvin, Files: 197
Folder: pins_Ben Affleck, Files: 126
Folder: pins_Bill Gates, Files: 122
Folder: pins_Bobby Morley, Files: 138
Folder: pins_Brenton Thwaites, Files: 209
Folder: pins_Brian J. Smith, Files: 102
Folder: pins_Brie Larson, Files: 169
Folder: pins_camila mendes, Files: 162
Folder: pins_Chris Evans, Files: 166
Folder: pins_Chris Hemsworth, Files: 159
Folder: pins_Chris Pratt, Files: 176
Folder: pins_Christian Bale, Files: 154
Folder: pins_Cristiano Ronaldo, Files: 98
Folder: pins_Danielle Panabaker, Files: 181
Folder: pins_Dominic Purcell, Files: 146
Folder: pins_Dwayne Johnson, Files: 141
Folder: pins_Eliza Taylor, Files: 162
Folder: pins_Elizabeth Lail, Files: 158
Folder: pins_elizabeth olsen, Files: 221
Folder: pins_ellen page, Files: 188
Folder: pins_elon musk, Files: 135
Folder: pins_Emilia Clarke, Files: 210
Folder: pins_Emma Stone, Files: 139
Folder: pins_Emma Watson, Files: 211
Folder: pins_gal gadot, Files: 199
Folder: pins_grant gustin, Files: 183
Folder: pins_Gwyneth Paltrow, Files: 187
Folder: pins_Henry Cavil, Files: 195
Folder: pins_Hugh Jackman, Files: 179
Folder: pins_Inbar Lavi, Files: 127
Folder: pins_Irina Shayk, Files: 156
Folder: pins_Jake Mcdorman, Files: 159
Folder: pins_Jason Momoa, Files: 184
Folder: pins_jeff bezos, Files: 106
Folder: pins_Jennifer Lawrence, Files: 180
Folder: pins_Jeremy Renner, Files: 167
Folder: pins_Jessica Barden, Files: 141
Folder: pins_Jimmy Fallon, Files: 113
Folder: pins_Johnny Depp, Files: 182
Folder: pins_Josh Radnor, Files: 117
Folder: pins_Katharine Mcphee, Files: 177
Folder: pins_Katherine Langford, Files: 226
Folder: pins_Keanu Reeves, Files: 160
Folder: pins_kiernen shipka, Files: 203
Folder: pins_Krysten Ritter, Files: 171
Folder: pins_Leonardo DiCaprio, Files: 237
Folder: pins_Lili Reinhart, Files: 150
Folder: pins_Lindsey Morgan, Files: 169
Folder: pins_Lionel Messi, Files: 86
Folder: pins_Logan Lerman, Files: 212
Folder: pins_Madelaine Petsch, Files: 192
Folder: pins_Maisie Williams, Files: 193
Folder: pins_margot robbie, Files: 221
Folder: pins_Maria Pedraza, Files: 122
Folder: pins_Marie Avgeropoulos, Files: 161
Folder: pins_Mark Ruffalo, Files: 178
Folder: pins_Mark Zuckerberg, Files: 95
Folder: pins_Megan Fox, Files: 209
Folder: pins_melissa fumero, Files: 154
Folder: pins_Miley Cyrus, Files: 178
Folder: pins_Millie Bobby Brown, Files: 191
Folder: pins_Morena Baccarin, Files: 175
Folder: pins_Morgan Freeman, Files: 105
Folder: pins_Nadia Hilker, Files: 133
Folder: pins_Natalie Dormer, Files: 198
Folder: pins_Natalie Portman, Files: 166
Folder: pins_Neil Patrick Harris, Files: 116
Folder: pins_Pedro Alonso, Files: 125
Folder: pins_Penn Badgley, Files: 171
Folder: pins_Rami Malek, Files: 160
Folder: pins_Rebecca Ferguson, Files: 178
Folder: pins_Richard Harmon, Files: 148
Folder: pins_Rihanna, Files: 133
Folder: pins_Robert De Niro, Files: 156
Folder: pins_Robert Downey Jr, Files: 233
Folder: pins_Sarah Wayne Callies, Files: 159
Folder: pins_scarlett johansson, Files: 201
Folder: pins_Selena Gomez, Files: 186
Folder: pins_Shakira Isabel Mebarak, Files: 154
Folder: pins_Sophie Turner, Files: 204
Folder: pins_Stephen Amell, Files: 159
Folder: pins_Taylor Swift, Files: 131
Folder: pins_Tom Cruise, Files: 192
Folder: pins_tom ellis, Files: 180
Folder: pins_Tom Hardy, Files: 198
Folder: pins_Tom Hiddleston, Files: 181
Folder: pins_Tom Holland, Files: 189
Folder: pins_Tuppence Middleton, Files: 133
Folder: pins_Ursula Corbero, Files: 167
Folder: pins_Wentworth Miller, Files: 179
Folder: pins_Zac Efron, Files: 191
Folder: pins_Zendaya, Files: 138
Folder: pins_Zoe Saldana, Files: 186

Number of folders: 105
Total number of files: 17534

# Specify the path to the main directory
main_directory = '105_classes_pins_dataset'

# Get a list of all folders
all_folders = [folder for folder in os.listdir(main_directory) if os.path.isdir(os.path.join(main_directory, folder))]

# Randomly pick 10 folders
selected_folders = random.sample(all_folders, 10)

# Create a 2x5 grid for displaying images
fig, axes = plt.subplots(2, 5, figsize=(10, 5))

# Iterate through selected folders and display one random image from each folder
for i, folder in enumerate(selected_folders):
    folder_path = os.path.join(main_directory, folder)
    all_images = [img for img in os.listdir(folder_path) if img.endswith('.jpg')]
    
    # Randomly pick one image
    selected_image = random.choice(all_images)
    
    # Get the full path to the selected image
    image_path = os.path.join(folder_path, selected_image)
    
    # Read and display the image
    img = mpimg.imread(image_path)
    axes[i // 5, i % 5].imshow(img)
    axes[i // 5, i % 5].axis('off')
    
    # Extract label from folder name and drop the first 5 characters
    label = folder[5:]
    axes[i // 5, i % 5].set_title(label)

# Adjust layout and show the plot
plt.tight_layout()
plt.show()

#!pip install mtcnn

import matplotlib.patches as patches
from mtcnn import MTCNN

# Function to perform face detection and display the image with bounding boxes
def detect_and_display_faces(image_path):
    # Load the image
    img = cv2.imread(image_path)
    
    # Convert the image to RGB for compatibility with MTCNN
    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
    # Load the pre-trained MTCNN model
    detector = MTCNN()
    
    # Perform face detection
    faces = detector.detect_faces(img_rgb)
    
    # Display the original image
    fig, ax = plt.subplots()
    ax.imshow(img_rgb)
    
    # Add bounding boxes for each detected face
    for face in faces:
        x, y, w, h = face['box']
        rect = patches.Rectangle((x, y), w, h, linewidth=2, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
    
    plt.axis('off')
    plt.show()

# Specify the path to the main directory
main_directory = '105_classes_pins_dataset'

# Get a list of all folders
all_folders = [folder for folder in os.listdir(main_directory) if os.path.isdir(os.path.join(main_directory, folder))]

# Randomly pick a folder
selected_folder = random.choice(all_folders)
folder_path = os.path.join(main_directory, selected_folder)

# Get a list of all images in the selected folder
all_images = [img for img in os.listdir(folder_path) if img.endswith('.jpg')]

# Randomly pick an image from the folder
selected_image = random.choice(all_images)
image_path = os.path.join(folder_path, selected_image)

# Perform face detection using MTCNN and display the result
detect_and_display_faces(image_path)

WARNING:tensorflow:From C:\Users\Ifham.Khan\anaconda3\lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.

WARNING:tensorflow:From C:\Users\Ifham.Khan\anaconda3\lib\site-packages\keras\src\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.

WARNING:tensorflow:From C:\Users\Ifham.Khan\anaconda3\lib\site-packages\keras\src\layers\pooling\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

1/1 [==============================] - 1s 1s/step
1/1 [==============================] - 1s 615ms/step
1/1 [==============================] - 0s 116ms/step
1/1 [==============================] - 0s 188ms/step
1/1 [==============================] - 0s 88ms/step
1/1 [==============================] - 0s 78ms/step
1/1 [==============================] - 0s 114ms/step
1/1 [==============================] - 0s 69ms/step
3/3 [==============================] - 1s 11ms/step
1/1 [==============================] - 0s 465ms/step

import os
import shutil

# Set the directory path
directory_path = 'working'

# Remove all files and subdirectories in the directory
for item in os.listdir(directory_path):
    item_path = os.path.join(directory_path, item)
    if os.path.isfile(item_path) or os.path.islink(item_path):
        os.unlink(item_path)
    elif os.path.isdir(item_path):
        shutil.rmtree(item_path)

print("Contents of the working directory cleared.")

Contents of the working directory cleared.

import shutil

# Source directory
source_directory = '105_classes_pins_dataset'

# Destination directory
destination_directory = 'working'

# Create train and test directories if not exist
train_directory = os.path.join(destination_directory, 'train')
os.makedirs(train_directory, exist_ok=True)


# Get a list of all folders in the source directory
all_folders = [folder for folder in os.listdir(source_directory) if os.path.isdir(os.path.join(source_directory, folder))]

# Iterate through each folder
for folder in all_folders:
    folder_path = os.path.join(source_directory, folder)
    
    # Get a list of all images in the folder
    all_images = [img for img in os.listdir(folder_path) if img.endswith('.jpg')]
    
    # Create a label for the folder (drop the first 5 characters)
    label = folder[5:]
    
    # Shuffle the images
    random.shuffle(all_images)
    
    # Move the first 85 images to the train directory
    for i in range(85):
        image_path = os.path.join(folder_path, all_images[i])
        destination_path = os.path.join(train_directory, label, all_images[i])
        os.makedirs(os.path.dirname(destination_path), exist_ok=True)
        shutil.copy(image_path, destination_path)
    

print("Data splitting completed.")

Data splitting completed.

from matplotlib.patches import Rectangle
from mtcnn.mtcnn import MTCNN

# Directory paths
train_directory = 'working/train'

# Get a list of all folders in the train directory
all_folders = [folder for folder in os.listdir(train_directory) if os.path.isdir(os.path.join(train_directory, folder))]

# Randomly select two folders
selected_folders = random.sample(all_folders, 2)

# Initialize MTCNN detector
detector = MTCNN()

# Iterate through selected folders
for folder in selected_folders:
    folder_path = os.path.join(train_directory, folder)
    
    # Get a list of all images in the folder
    all_images = [img for img in os.listdir(folder_path) if img.endswith('.jpg')]
    
    # Randomly select one image
    selected_image = random.choice(all_images)
    image_path = os.path.join(folder_path, selected_image)
    
    # Read the image
    image = cv2.imread(image_path)
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    # Detect faces using MTCNN
    faces = detector.detect_faces(image)
    
    # Display image with bounding boxes around detected faces
    plt.figure(figsize=(8, 3))
    
    # Display original image
    plt.subplot(1, 2, 1)
    plt.imshow(image_rgb)
    plt.title('Original Image')
    
    # Display image with bounding boxes
    plt.subplot(1, 2, 2)
    plt.imshow(image_rgb)
    
    for face in faces:
        x, y, width, height = face['box']
        rect = Rectangle((x, y), width, height, fill=False, color='red')
        plt.gca().add_patch(rect)
        
        # Display additional keypoints
        for key, value in face['keypoints'].items():
            plt.scatter(value[0], value[1], s=30, color='blue', marker='o')
            plt.text(value[0] + 5, value[1], key, color='blue')
    
    plt.title('Detected Faces with Keypoints')
    plt.show()

    # Display metadata of detected faces
    print(f"Metadata of detected faces in {folder}/{selected_image}:")
    for i, face in enumerate(faces):
        print(f"Face {i + 1}:")
        print(f"   Confidence: {face['confidence']:.2f}")
        print(f"   Bounding Box: {face['box']}")
        print(f"   Keypoints: {face['keypoints']}")
        print()

1/1 [==============================] - 0s 372ms/step
WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000019C9A371B80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
1/1 [==============================] - 0s 339ms/step
1/1 [==============================] - 0s 47ms/step
1/1 [==============================] - 0s 63ms/step
1/1 [==============================] - 0s 64ms/step
1/1 [==============================] - 0s 65ms/step
1/1 [==============================] - 0s 70ms/step
1/1 [==============================] - 0s 62ms/step
WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000019C9A3B9550> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
3/3 [==============================] - 0s 16ms/step
1/1 [==============================] - 0s 379ms/step

Metadata of detected faces in Chris Hemsworth/Chris Hemsworth152_415.jpg:
Face 1:
   Confidence: 1.00
   Bounding Box: [40, 38, 155, 205]
   Keypoints: {'left_eye': (89, 107), 'right_eye': (162, 109), 'nose': (131, 146), 'mouth_left': (93, 188), 'mouth_right': (155, 189)}

1/1 [==============================] - 0s 53ms/step
1/1 [==============================] - 0s 57ms/step
1/1 [==============================] - 0s 78ms/step
1/1 [==============================] - 0s 41ms/step
1/1 [==============================] - 0s 45ms/step
1/1 [==============================] - 0s 64ms/step
1/1 [==============================] - 0s 54ms/step
1/1 [==============================] - 0s 41ms/step
1/1 [==============================] - 0s 74ms/step
1/1 [==============================] - 0s 78ms/step

Metadata of detected faces in Stephen Amell/Stephen Amell188_4150.jpg:
Face 1:
   Confidence: 1.00
   Bounding Box: [32, 48, 148, 194]
   Keypoints: {'left_eye': (87, 122), 'right_eye': (153, 120), 'nose': (133, 157), 'mouth_left': (98, 195), 'mouth_right': (152, 194)}

#!pip install facenet-pytorch

from facenet_pytorch import MTCNN, InceptionResnetV1, extract_face
from PIL import Image

# Function to process image, detect face, and calculate embeddings
def process_image(image_path, face_detector, face_embedder, label):
    # Read the image
    image = cv2.imread(image_path)
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Detect faces using MTCNN
    boxes, probs = face_detector.detect(image_rgb)

    if boxes is not None:
        # Display label of the image using the folder it is picked up from
        plt.title(f"Label: {label}")

        # Display original image
        plt.imshow(Image.fromarray(image_rgb))

        # Draw a rectangle around each detected face on the original image
        for i, box in enumerate(boxes):
            x, y, w, h = box
            # Draw a rectangle around the detected face with thinner lines
            cv2.rectangle(image_rgb, (int(x), int(y)), (int(x + w), int(y + h)), (255, 0, 0), 1)

        # Display the image with the rectangles around detected faces
        plt.imshow(Image.fromarray(image_rgb))

        plt.axis('off')
        plt.show()

        # Apply transformations and normalization
        transformed_faces = [face_embedder(extract_face(image_rgb, box).unsqueeze(0)) for box in boxes]

        # Convert PyTorch tensor to NumPy array
        transformed_faces_np = [transformed_face.squeeze().detach().numpy() for transformed_face in transformed_faces]

        # Print the embeddings
        for i, embeddings in enumerate(transformed_faces_np):
            print(f"Embeddings for Detected Face {i + 1}:", embeddings)

# Directory paths
train_directory = 'working/train/'

# Randomly pick a folder
selected_folder = random.choice(os.listdir(train_directory))
selected_folder_path = os.path.join(train_directory, selected_folder)

# Randomly pick one file from the selected folder
file = random.choice(os.listdir(selected_folder_path))
file_path = os.path.join(selected_folder_path, file)

# Initialize MTCNN for face detection with adjusted parameters
face_detector = MTCNN(margin=20, post_process=False, select_largest=False)
   
# Initialize InceptionResnetV1 for face embedding
face_embedder = InceptionResnetV1(pretrained='vggface2').eval()

# Process image, detect faces, calculate embeddings, and display results
process_image(file_path, face_detector, face_embedder, label=selected_folder)

Embeddings for Detected Face 1: [ 5.71297370e-02 -2.67934948e-02  3.81031856e-02  2.27163546e-02
  3.57267410e-02 -6.98381066e-02 -2.50966903e-02 -3.76983196e-03
 -2.96353945e-03 -5.07798344e-02 -8.72919708e-02 -6.77055344e-02
 -9.40293223e-02 -2.22640000e-02  7.56724328e-02 -2.69111544e-02
  5.44652343e-04  1.07508589e-04  3.00200358e-02  2.49170102e-02
  1.88040566e-02  2.91414633e-02 -5.01331575e-02 -1.18766641e-02
  1.39893349e-02 -3.66233126e-03  7.53469244e-02 -1.79457758e-02
  2.41288971e-02 -8.23454484e-02 -2.90093310e-02  5.40296882e-02
  2.29179431e-02  4.32333685e-02  6.66124374e-02  1.38067510e-02
 -4.40900922e-02  3.39963213e-02 -3.49919535e-02  2.30008345e-02
  8.09066556e-03  5.58163133e-03 -8.46079434e-04 -1.09960034e-01
 -2.18510404e-02 -6.12194985e-02  2.36308556e-02 -3.42507139e-02
 -4.37819436e-02  3.52482423e-02  1.08578905e-01  6.88848719e-02
  5.53716980e-02  7.22644106e-02 -8.98449216e-03 -6.90328181e-02
  4.13697958e-02  6.15122542e-02  3.99996946e-03  2.58536022e-02
 -2.63578054e-02 -1.01613522e-01  6.42609447e-02 -2.93224715e-02
  5.77571848e-03  3.06629874e-02  6.16656207e-02  3.42602879e-02
  2.49051359e-02 -8.20538551e-02  2.25909501e-02 -8.62764716e-02
 -6.27406081e-03 -1.81139708e-02 -1.16130086e-02  3.07787713e-02
  8.47872794e-02  2.21681464e-02 -1.50854737e-02  6.55373335e-02
  2.66668741e-02  6.11215122e-02  2.07586400e-02 -2.35098116e-02
 -1.82960592e-02  3.43521982e-02  3.36214378e-02 -1.07448502e-02
  7.47946836e-03  1.11695327e-01 -2.64298003e-02  5.35408147e-02
  5.71246259e-03 -4.95980978e-02 -5.93818538e-02  1.94376912e-02
 -3.61155495e-02  2.19532959e-02 -8.84989649e-02 -1.02047943e-01
 -3.03038526e-02 -3.96082588e-02  3.93114937e-03 -5.22588491e-02
  4.90954965e-02  1.71754826e-02  1.32309020e-01  1.08675109e-02
 -2.96060345e-04 -2.63486300e-02 -3.43204252e-02 -8.82319659e-02
  6.41116221e-03  1.05120335e-02 -1.84318447e-03  2.72151846e-02
  6.81786984e-03 -2.93561164e-02  1.80993375e-04  3.43012288e-02
 -8.48892704e-02 -5.98186860e-03  1.16549265e-02  5.41776419e-02
 -2.49393675e-02 -1.00599863e-02 -1.46408826e-02 -8.68028402e-02
 -1.24875475e-02  2.34454516e-02  2.06508040e-02  4.11290936e-02
  1.87859703e-02 -9.13568586e-02  1.99332386e-02  2.09055096e-02
  2.44836584e-02 -5.91235375e-03 -2.88535021e-02 -8.15733224e-02
  7.70698339e-02 -1.89777352e-02 -3.01886294e-02  1.73313282e-02
 -2.85990797e-02  1.81605387e-02 -2.57706512e-02 -1.65923629e-02
 -7.19916541e-04 -4.15094569e-03 -7.46959541e-03  3.93360667e-02
 -7.97324255e-02 -1.59871373e-02 -6.74565211e-02  7.26374332e-03
  5.65960780e-02  1.70974396e-02  9.78219323e-03  3.48697975e-02
 -3.86136235e-03 -2.11567767e-02  3.43021303e-02 -5.70137687e-02
 -2.99644116e-02  1.09251831e-02 -1.02776110e-01  2.93914881e-02
  4.26594727e-02  5.34811104e-03 -1.85193326e-02 -4.54982035e-02
 -2.71403641e-02 -8.55365619e-02 -1.27494885e-02 -4.46985066e-02
 -5.37515692e-02 -1.10088654e-01 -1.70345753e-02 -2.71163173e-02
 -3.45073938e-02  9.23171341e-02 -1.69338807e-02 -1.95366200e-02
 -1.63072608e-02  1.59206185e-02 -6.38923645e-02 -3.99290621e-02
  7.94148771e-04  1.07785262e-01 -9.50809196e-03  3.53800394e-02
 -2.50735115e-02  1.16082884e-01 -2.18565818e-02  3.17009129e-02
 -2.07136609e-02 -1.01471636e-02 -3.60553600e-02 -3.15122865e-02
 -2.85117188e-04 -2.75774952e-02 -2.38530338e-02  1.24528721e-01
 -4.65829596e-02 -9.19998065e-02  1.44233322e-02  6.61518648e-02
 -8.79022554e-02  9.18559507e-02  3.55658447e-03 -2.76046898e-02
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from facenet_pytorch import MTCNN, InceptionResnetV1, extract_face
from PIL import Image
from sklearn.metrics.pairwise import euclidean_distances

# Function to process images, detect faces, and calculate embeddings
def process_images(folder_path1, file1, folder_path2, file2, face_detector, face_embedder):
    # Read the images
    image1_path = os.path.join(folder_path1, file1)
    image2_path = os.path.join(folder_path2, file2)

    image1 = cv2.imread(image1_path)
    image1_rgb = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

    image2 = cv2.imread(image2_path)
    image2_rgb = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)

    # Detect faces using MTCNN
    faces1, _ = face_detector.detect(image1_rgb)
    faces2, _ = face_detector.detect(image2_rgb)

    # Get labels from folder names
    label1 = os.path.basename(folder_path1)
    label2 = os.path.basename(folder_path2)

    # Display original images with rectangles around detected faces
    plt.subplot(1, 2, 1)
    plt.imshow(Image.fromarray(image1_rgb))
    plt.title(f"Original Image 1\nLabel: {label1}")

    for i, face in enumerate(faces1):
        x, y, w, h = face
        # Draw a rectangle around the detected face
        cv2.rectangle(image1_rgb, (int(x), int(y)), (int(x+w), int(y+h)), (255, 0, 0), 2)

    plt.subplot(1, 2, 2)
    plt.imshow(Image.fromarray(image1_rgb))
    plt.title(f"Detected Faces 1")
    plt.axis('off')

    plt.show()

    plt.subplot(1, 2, 1)
    plt.imshow(Image.fromarray(image2_rgb))
    plt.title(f"Original Image 2\nLabel: {label2}")

    for i, face in enumerate(faces2):
        x, y, w, h = face
        # Draw a rectangle around the detected face
        cv2.rectangle(image2_rgb, (int(x), int(y)), (int(x+w), int(y+h)), (255, 0, 0), 2)

    plt.subplot(1, 2, 2)
    plt.imshow(Image.fromarray(image2_rgb))
    plt.title(f"Detected Faces 2")
    plt.axis('off')

    plt.show()

    # Calculate embeddings for the first image
    embeddings1 = [face_embedder(extract_face(image1_rgb, face).unsqueeze(0)).squeeze().detach().numpy() for face in faces1]

    # Print the embeddings for the first image
    #for i, embeddings in enumerate(embeddings1):
     #   print(f"Embeddings for Detected Face {i + 1} in Image 1:", embeddings)

    # Calculate embeddings for the second image
    embeddings2 = [face_embedder(extract_face(image2_rgb, face).unsqueeze(0)).squeeze().detach().numpy() for face in faces2]

    # Print the embeddings for the second image
   # for i, embeddings in enumerate(embeddings2):
    #    print(f"Embeddings for Detected Face {i + 1} in Image 2:", embeddings)

    # Calculate distance between the two embeddings
    distance = euclidean_distances(embeddings1[0].reshape(1, -1), embeddings2[0].reshape(1, -1))[0][0]

    # Display the distance between the two embeddings
    print(f"Distance between embeddings: {distance:.4f}")

# Directory paths
train_directory = 'working/train/'

# Randomly pick two folders
selected_folders = random.sample(os.listdir(train_directory), 2)

# Randomly pick one file from each selected folder
file1 = random.choice(os.listdir(os.path.join(train_directory, selected_folders[0])))
file2 = random.choice(os.listdir(os.path.join(train_directory, selected_folders[1])))

# Initialize MTCNN for face detection
face_detector = MTCNN(keep_all=True)

# Initialize InceptionResnetV1 for face embedding
face_embedder = InceptionResnetV1(pretrained='vggface2').eval()

# Process images, detect faces, calculate embeddings, and display results
process_images(
    os.path.join(train_directory, selected_folders[0]),
    file1,
    os.path.join(train_directory, selected_folders[1]),
    file2,
    face_detector,
    face_embedder
)

Distance between embeddings: 0.3837

from facenet_pytorch import MTCNN, InceptionResnetV1, extract_face
from PIL import Image
from sklearn.metrics.pairwise import euclidean_distances

# Function to process images, detect faces, and calculate embeddings
def process_images(folder_path, face_detector, face_embedder):
    # Randomly pick two files from the selected folder
    files = random.sample(os.listdir(folder_path), 2)
    
    # Read the images
    image1_path = os.path.join(folder_path, files[0])
    image2_path = os.path.join(folder_path, files[1])

    image1 = cv2.imread(image1_path)
    image1_rgb = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)

    image2 = cv2.imread(image2_path)
    image2_rgb = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)

    # Detect faces using MTCNN
    faces1, _ = face_detector.detect(image1_rgb)
    faces2, _ = face_detector.detect(image2_rgb)

    # Get the label from the folder name
    label = os.path.basename(folder_path)

    # Display original images with rectangles around detected faces
    plt.subplot(1, 2, 1)
    plt.imshow(Image.fromarray(image1_rgb))
    plt.title(f"Original Image 1\nLabel: {label}")

    for i, face in enumerate(faces1):
        x, y, w, h = face
        # Draw a rectangle around the detected face
        cv2.rectangle(image1_rgb, (int(x), int(y)), (int(x+w), int(y+h)), (255, 0, 0), 2)

    plt.subplot(1, 2, 2)
    plt.imshow(Image.fromarray(image1_rgb))
    plt.title(f"Detected Faces 1")
    plt.axis('off')

    plt.show()

    plt.subplot(1, 2, 1)
    plt.imshow(Image.fromarray(image2_rgb))
    plt.title(f"Original Image 2\nLabel: {label}")

    for i, face in enumerate(faces2):
        x, y, w, h = face
        # Draw a rectangle around the detected face
        cv2.rectangle(image2_rgb, (int(x), int(y)), (int(x+w), int(y+h)), (255, 0, 0), 2)

    plt.subplot(1, 2, 2)
    plt.imshow(Image.fromarray(image2_rgb))
    plt.title(f"Detected Faces 2")
    plt.axis('off')

    plt.show()

    # Calculate embeddings for the first image
    embeddings1 = [face_embedder(extract_face(image1_rgb, face).unsqueeze(0)).squeeze().detach().numpy() for face in faces1]

    # Print the embeddings for the first image
    #for i, embeddings in enumerate(embeddings1):
     #   print(f"Embeddings for Detected Face {i + 1} in Image 1:", embeddings)

    # Calculate embeddings for the second image
    embeddings2 = [face_embedder(extract_face(image2_rgb, face).unsqueeze(0)).squeeze().detach().numpy() for face in faces2]

    # Print the embeddings for the second image
   # for i, embeddings in enumerate(embeddings2):
    #    print(f"Embeddings for Detected Face {i + 1} in Image 2:", embeddings)

    # Calculate distance between the two embeddings
    distance = euclidean_distances(embeddings1[0].reshape(1, -1), embeddings2[0].reshape(1, -1))[0][0]

    # Display the distance between the two embeddings
    print(f"Distance between embeddings: {distance:.4f}")

# Directory paths
train_directory = 'working/train/'

# Randomly pick one folder
selected_folder = random.choice(os.listdir(train_directory))
selected_folder_path = os.path.join(train_directory, selected_folder)

# Initialize MTCNN for face detection
face_detector = MTCNN(keep_all=True)

# Initialize InceptionResnetV1 for face embedding
face_embedder = InceptionResnetV1(pretrained='vggface2').eval()

# Process images, detect faces, calculate embeddings, and display results
process_images(selected_folder_path, face_detector, face_embedder)

Distance between embeddings: 0.3055

import os
import shutil

# Set the directory path
directory_path = 'working/train_detected_faces'

# Create the directory if it doesn't exist
if not os.path.exists(directory_path):
    os.makedirs(directory_path)
    print(f"Directory '{directory_path}' created.")

# Remove all files and subdirectories in the directory
for item in os.listdir(directory_path):
    item_path = os.path.join(directory_path, item)
    if os.path.isfile(item_path) or os.path.islink(item_path):
        os.unlink(item_path)
    elif os.path.isdir(item_path):
        shutil.rmtree(item_path)

print("Contents of train_detected_face cleared.")

Directory 'working/train_detected_faces' created.
Contents of train_detected_face cleared.

import os
import cv2
import numpy as np

data_directory = 'C:\\Users\\Ifham.Khan\\Desktop\\Final Year Project\\105_classes_pins_dataset'
output_directory = 'C:\\Users\\Ifham.Khan\\Desktop\\Final Year Project\\working\\train_detected_faces'

def detect_faces_and_save(image_paths, output_directory):
    processed_images = []
    images_without_faces = 0

    for i, image_path in enumerate(image_paths):
        img = cv2.imread(image_path)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Use OpenCV's deep learning-based face detector
        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)

        if len(faces) > 0:
            # Assuming only one face is detected, adjust the dimensions
            x, y, w, h = faces[0]
            face_roi = img[y:y+h, x:x+w]
            resized_face = cv2.resize(face_roi, (224, 224))

            # Get the original folder name
            folder_name = image_path.split('\\')[-2]
            # Create the output folder if it doesn't exist
            output_folder = os.path.join(output_directory, folder_name)
            os.makedirs(output_folder, exist_ok=True)

            # Save the detected face with the same folder structure
            output_path = os.path.join(output_folder, f"detected_face_{i}.jpg")
            cv2.imwrite(output_path, resized_face)

            processed_images.append(resized_face)
        else:
            images_without_faces += 1

        if i % 50 == 0:
            print(f"{i}/{len(image_paths)} images processed", end='\r', flush=True)

    print(f"\nImages without faces detected/Total images: {images_without_faces}/{len(image_paths)}")
    return np.array(processed_images)

# Create output directory if it doesn't exist
os.makedirs(output_directory, exist_ok=True)

# Get list of all image paths
all_image_paths = []
for folder_name in os.listdir(data_directory):
    folder_path = os.path.join(data_directory, folder_name)
    if os.path.isdir(folder_path):
        image_paths = [os.path.join(folder_path, image_name) for image_name in os.listdir(folder_path)]
        all_image_paths.extend(image_paths)

X_all_processed = detect_faces_and_save(all_image_paths, output_directory)

print("Face detection and saving complete.")

17500/17534 images processed
Images without faces detected/Total images: 4069/17534
Face detection and saving complete.

import os
import cv2
import matplotlib.pyplot as plt
import numpy as np

# Replace this with the path to your detected faces directory
output_directory = 'working/train_detected_faces'

# Get a list of subdirectories (folders) in the output directory
folders = [f for f in os.listdir(output_directory) if os.path.isdir(os.path.join(output_directory, f))]

# Randomly pick five folders (allowing replacement if needed)
selected_folders = np.random.choice(folders, size=min(5, len(folders)), replace=True)

# Set up the subplot
fig, axes = plt.subplots(nrows=len(selected_folders), ncols=5, figsize=(15, 15))

for i, folder_name in enumerate(selected_folders):
    folder_path = os.path.join(output_directory, folder_name)
    image_paths = [os.path.join(folder_path, image_name) for image_name in os.listdir(folder_path)[:5]]

    for j, image_path in enumerate(image_paths):
        # Read the image using OpenCV
        img = cv2.imread(image_path)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # Display the image
        axes[i, j].imshow(img)
        axes[i, j].set_title(f'{folder_name} - {j+1}')
        axes[i, j].axis('off')

plt.tight_layout()
plt.show()

#!pip install facenet-pytorch

from facenet_pytorch import InceptionResnetV1
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import euclidean_distances
import torch

# Function to process images and calculate embeddings
def process_images(face1_path, face2_path, face_embedder):
    # Read the pre-detected face images
    face1 = cv2.imread(face1_path)
    face1_rgb = cv2.cvtColor(face1, cv2.COLOR_BGR2RGB)

    face2 = cv2.imread(face2_path)
    face2_rgb = cv2.cvtColor(face2, cv2.COLOR_BGR2RGB)

    # Get labels from folder names
    label1 = os.path.basename(os.path.dirname(face1_path))
    label2 = os.path.basename(os.path.dirname(face2_path))

    # Display original face images
    plt.subplot(1, 2, 1)
    plt.imshow(Image.fromarray(face1_rgb))
    plt.title(f"Original Face 1\nLabel: {label1}")
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.imshow(Image.fromarray(face2_rgb))
    plt.title(f"Original Face 2\nLabel: {label2}")
    plt.axis('off')

    plt.show()

    # Convert NumPy arrays to PyTorch tensors
    face1_tensor = torch.from_numpy(face1_rgb.transpose(2, 0, 1)).unsqueeze(0).float() / 255.0
    face2_tensor = torch.from_numpy(face2_rgb.transpose(2, 0, 1)).unsqueeze(0).float() / 255.0

    # Calculate embeddings for the first face
    embeddings1 = face_embedder(face1_tensor).detach().numpy()

    # Print the embeddings for the first face
    # print("Embeddings for Face 1:", embeddings1)

    # Calculate embeddings for the second face
    embeddings2 = face_embedder(face2_tensor).detach().numpy()

    # Print the embeddings for the second face
    # print("Embeddings for Face 2:", embeddings2)

    # Calculate distance between the two embeddings
    distance = euclidean_distances(embeddings1.reshape(1, -1), embeddings2.reshape(1, -1))[0][0]

    # Display the distance between the two embeddings
    print(f"Distance between embeddings: {distance:.4f}")

# Directory paths
train_detected_faces_directory = 'working/train_detected_faces'

# Get a list of folders in the train_detected_faces_directory
folders = [f for f in os.listdir(train_detected_faces_directory) if os.path.isdir(os.path.join(train_detected_faces_directory, f))]

# Ensure there are at least two folders
if len(folders) >= 2:
    # Randomly pick two folders
    selected_folders = random.sample(folders, 2)

    # Randomly pick one file from each selected folder
    file1 = random.choice(os.listdir(os.path.join(train_detected_faces_directory, selected_folders[0])))
    file2 = random.choice(os.listdir(os.path.join(train_detected_faces_directory, selected_folders[1])))

    # Full paths for the selected faces
    face1_path = os.path.join(train_detected_faces_directory, selected_folders[0], file1)
    face2_path = os.path.join(train_detected_faces_directory, selected_folders[1], file2)

    # Initialize InceptionResnetV1 for face embedding
    face_embedder = InceptionResnetV1(pretrained='vggface2').eval()

    # Process face images, calculate embeddings, and display results
    process_images(face1_path, face2_path, face_embedder)
else:
    print("Not enough folders in the specified directory.")

Distance between embeddings: 1.1345

import os
import random
from facenet_pytorch import InceptionResnetV1
from PIL import Image
import matplotlib.pyplot as plt
import cv2
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
import torch

# Function to process images and calculate embeddings
def process_images(folder_path, face1_file, face2_file, face_embedder):
    # Read the pre-detected face images
    face1_path = os.path.join(folder_path, face1_file)
    face2_path = os.path.join(folder_path, face2_file)

    face1 = cv2.imread(face1_path)
    face1_rgb = cv2.cvtColor(face1, cv2.COLOR_BGR2RGB)

    face2 = cv2.imread(face2_path)
    face2_rgb = cv2.cvtColor(face2, cv2.COLOR_BGR2RGB)

    # Display original face images
    plt.subplot(1, 2, 1)
    plt.imshow(Image.fromarray(face1_rgb))
    plt.title(f"Original Face 1\nFolder: {os.path.basename(folder_path)}")
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.imshow(Image.fromarray(face2_rgb))
    plt.title(f"Original Face 2\nFolder: {os.path.basename(folder_path)}")
    plt.axis('off')

    plt.show()

    # Convert NumPy arrays to PyTorch tensors
    face1_tensor = torch.from_numpy(face1_rgb.transpose(2, 0, 1)).unsqueeze(0).float() / 255.0
    face2_tensor = torch.from_numpy(face2_rgb.transpose(2, 0, 1)).unsqueeze(0).float() / 255.0

    # Calculate embeddings for the first face
    embeddings1 = face_embedder(face1_tensor).detach().numpy()

    # Print the embeddings for the first face
    # print("Embeddings for Face 1:", embeddings1)

    # Calculate embeddings for the second face
    embeddings2 = face_embedder(face2_tensor).detach().numpy()

    # Print the embeddings for the second face
    # print("Embeddings for Face 2:", embeddings2)

    # Calculate distance between the two embeddings
    distance = euclidean_distances(embeddings1.reshape(1, -1), embeddings2.reshape(1, -1))[0][0]

    # Display the distance between the two embeddings
    print(f"Distance between embeddings: {distance:.4f}")

# Directory paths
train_detected_faces_directory = 'working/train_detected_faces'

# Randomly pick one folder
selected_folder = random.choice(os.listdir(train_detected_faces_directory))
folder_path = os.path.join(train_detected_faces_directory, selected_folder)

# Randomly pick two different files from the selected folder
files = random.sample(os.listdir(folder_path), 2)
face1_file, face2_file = files

# Initialize InceptionResnetV1 for face embedding
face_embedder = InceptionResnetV1(pretrained='vggface2').eval()

# Process face images, calculate embeddings, and display results
process_images(folder_path, face1_file, face2_file, face_embedder)

Distance between embeddings: 0.8947

import os

output_directory = 'working/extracted_embeddings'
os.makedirs(output_directory, exist_ok=True)
print(f"Directory '{output_directory}' created.")

Directory 'working/extracted_embeddings' created.

import shutil
import os

folder_path = 'working/extracted_embeddings'

# Check if the folder exists before deleting
if os.path.exists(folder_path):
    shutil.rmtree(folder_path)
    print(f"The folder {folder_path} has been deleted.")
else:
    print(f"The folder {folder_path} does not exist.")

The folder working/extracted_embeddings has been deleted.

import torch
from facenet_pytorch import InceptionResnetV1
from PIL import Image
from torchvision import transforms
from tqdm import tqdm

# Function to extract embeddings from a single folder with data augmentation
def extract_embeddings_from_folder(folder_path, face_embedder, device, output_directory):
    embeddings = {}
    data_transform = transforms.Compose([
        transforms.RandomHorizontalFlip(),
        transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),
        transforms.RandomRotation(10),
        transforms.ToTensor()
    ])

    label = os.path.basename(folder_path)  # Get the label from the folder name

    for image_name in tqdm(os.listdir(folder_path), desc=f"Processing {folder_path}"):
        image_path = os.path.join(folder_path, image_name)
        try:
            img = Image.open(image_path)

            # Convert image to tensor
            img_tensor = transforms.ToTensor()(img).unsqueeze(0).float().to(device)

            # Calculate embedding
            embedding = face_embedder(img_tensor).squeeze().detach().cpu().numpy()
            embeddings[image_name] = embedding

            # Save the embedding for the original image
            output_emb_path = os.path.join(output_directory, f"{label}_{os.path.splitext(image_name)[0]}_embedding.npy")
            np.save(output_emb_path, embedding)

            # Apply data augmentation
            augmented_img = data_transform(img)

            # Convert augmented image to tensor
            img_tensor_augmented = augmented_img.unsqueeze(0).float().to(device)

            # Calculate embedding for the augmented image
            embedding_augmented = face_embedder(img_tensor_augmented).squeeze().detach().cpu().numpy()
            embeddings[f"{os.path.splitext(image_name)[0]}_augmented_embedding.npy"] = embedding_augmented

            # Save the embedding for the augmented image
            output_emb_path_augmented = os.path.join(output_directory, f"{label}_{os.path.splitext(image_name)[0]}_augmented_embedding.npy")
            np.save(output_emb_path_augmented, embedding_augmented)

        except Exception as e:
            print(f"Error processing {image_name}: {str(e)}")

    return label, embeddings

# Directory paths
input_directory = 'working/train_detected_faces'
output_directory = 'working/extracted_embeddings'

# Initialize InceptionResnetV1 for face embedding
device = 'cpu'
#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
face_embedder = InceptionResnetV1(pretrained='vggface2').eval().to(device)

# Loop through each folder in the input directory
for folder_name in os.listdir(input_directory):
    folder_path = os.path.join(input_directory, folder_name)

    # Create subfolder in output_directory for the current label
    label_output_directory = os.path.join(output_directory, folder_name)
    os.makedirs(label_output_directory, exist_ok=True)

    # Extract embeddings from the current folder with data augmentation
    label, embeddings = extract_embeddings_from_folder(folder_path, face_embedder, device, label_output_directory)

print("Embeddings extraction complete.")

Processing working/train_detected_faces\pins_Adriana Lima: 100%|█████████████████████| 186/186 [02:04<00:00,  1.50it/s]
Processing working/train_detected_faces\pins_Alex Lawther: 100%|███████████████████████| 86/86 [00:54<00:00,  1.58it/s]
Processing working/train_detected_faces\pins_Alexandra Daddario: 100%|███████████████| 206/206 [02:10<00:00,  1.58it/s]
Processing working/train_detected_faces\pins_Alvaro Morte: 100%|███████████████████████| 94/94 [01:01<00:00,  1.54it/s]
Processing working/train_detected_faces\pins_alycia dabnem carey: 100%|██████████████| 157/157 [01:40<00:00,  1.57it/s]
Processing working/train_detected_faces\pins_Amanda Crew: 100%|██████████████████████| 110/110 [01:09<00:00,  1.59it/s]
Processing working/train_detected_faces\pins_amber heard: 100%|██████████████████████| 166/166 [01:49<00:00,  1.52it/s]
Processing working/train_detected_faces\pins_Andy Samberg: 100%|█████████████████████| 167/167 [01:46<00:00,  1.56it/s]
Processing working/train_detected_faces\pins_Anne Hathaway: 100%|████████████████████| 157/157 [01:41<00:00,  1.54it/s]
Processing working/train_detected_faces\pins_Anthony Mackie: 100%|█████████████████████| 78/78 [00:50<00:00,  1.55it/s]
Processing working/train_detected_faces\pins_Avril Lavigne: 100%|████████████████████| 108/108 [01:10<00:00,  1.53it/s]
Processing working/train_detected_faces\pins_barack obama: 100%|███████████████████████| 87/87 [00:55<00:00,  1.58it/s]
Processing working/train_detected_faces\pins_barbara palvin: 100%|███████████████████| 144/144 [01:31<00:00,  1.57it/s]
Processing working/train_detected_faces\pins_Ben Affleck: 100%|████████████████████████| 98/98 [01:01<00:00,  1.58it/s]
Processing working/train_detected_faces\pins_Bill Gates: 100%|█████████████████████████| 69/69 [00:46<00:00,  1.47it/s]
Processing working/train_detected_faces\pins_Bobby Morley: 100%|███████████████████████| 69/69 [00:44<00:00,  1.56it/s]
Processing working/train_detected_faces\pins_Brenton Thwaites: 100%|█████████████████| 159/159 [01:43<00:00,  1.54it/s]
Processing working/train_detected_faces\pins_Brian J. Smith: 100%|█████████████████████| 78/78 [00:50<00:00,  1.55it/s]
Processing working/train_detected_faces\pins_Brie Larson: 100%|██████████████████████| 131/131 [01:24<00:00,  1.54it/s]
Processing working/train_detected_faces\pins_camila mendes: 100%|████████████████████| 130/130 [01:26<00:00,  1.50it/s]
Processing working/train_detected_faces\pins_Chris Evans: 100%|██████████████████████| 111/111 [01:12<00:00,  1.54it/s]
Processing working/train_detected_faces\pins_Chris Hemsworth: 100%|██████████████████| 133/133 [01:24<00:00,  1.57it/s]
Processing working/train_detected_faces\pins_Chris Pratt: 100%|██████████████████████| 158/158 [01:42<00:00,  1.53it/s]
Processing working/train_detected_faces\pins_Christian Bale: 100%|███████████████████| 129/129 [01:23<00:00,  1.54it/s]
Processing working/train_detected_faces\pins_Cristiano Ronaldo: 100%|██████████████████| 76/76 [00:49<00:00,  1.55it/s]
Processing working/train_detected_faces\pins_Danielle Panabaker: 100%|███████████████| 149/149 [01:41<00:00,  1.47it/s]
Processing working/train_detected_faces\pins_Dominic Purcell: 100%|██████████████████| 108/108 [01:08<00:00,  1.57it/s]
Processing working/train_detected_faces\pins_Dwayne Johnson: 100%|█████████████████████| 78/78 [00:52<00:00,  1.48it/s]
Processing working/train_detected_faces\pins_Eliza Taylor: 100%|█████████████████████| 130/130 [01:24<00:00,  1.55it/s]
Processing working/train_detected_faces\pins_Elizabeth Lail: 100%|███████████████████| 129/129 [01:23<00:00,  1.55it/s]
Processing working/train_detected_faces\pins_elizabeth olsen: 100%|██████████████████| 194/194 [02:04<00:00,  1.56it/s]
Processing working/train_detected_faces\pins_ellen page: 100%|███████████████████████| 157/157 [01:40<00:00,  1.56it/s]
Processing working/train_detected_faces\pins_elon musk: 100%|████████████████████████| 103/103 [01:05<00:00,  1.57it/s]
Processing working/train_detected_faces\pins_Emilia Clarke: 100%|████████████████████| 159/159 [01:41<00:00,  1.57it/s]
Processing working/train_detected_faces\pins_Emma Stone: 100%|███████████████████████| 100/100 [01:05<00:00,  1.53it/s]
Processing working/train_detected_faces\pins_Emma Watson: 100%|██████████████████████| 162/162 [01:44<00:00,  1.55it/s]
Processing working/train_detected_faces\pins_gal gadot: 100%|████████████████████████| 134/134 [01:24<00:00,  1.59it/s]
Processing working/train_detected_faces\pins_grant gustin: 100%|█████████████████████| 148/148 [01:35<00:00,  1.56it/s]
Processing working/train_detected_faces\pins_Gwyneth Paltrow: 100%|██████████████████| 160/160 [01:42<00:00,  1.56it/s]
Processing working/train_detected_faces\pins_Henry Cavil: 100%|██████████████████████| 144/144 [01:52<00:00,  1.27it/s]
Processing working/train_detected_faces\pins_Hugh Jackman: 100%|█████████████████████| 144/144 [01:56<00:00,  1.24it/s]
Processing working/train_detected_faces\pins_Inbar Lavi: 100%|███████████████████████| 110/110 [00:53<00:00,  2.07it/s]
Processing working/train_detected_faces\pins_Irina Shayk: 100%|██████████████████████| 137/137 [00:50<00:00,  2.73it/s]
Processing working/train_detected_faces\pins_Jake Mcdorman: 100%|████████████████████| 147/147 [00:53<00:00,  2.73it/s]
Processing working/train_detected_faces\pins_Jason Momoa: 100%|██████████████████████| 143/143 [00:51<00:00,  2.80it/s]
Processing working/train_detected_faces\pins_jeff bezos: 100%|█████████████████████████| 77/77 [00:27<00:00,  2.80it/s]
Processing working/train_detected_faces\pins_Jennifer Lawrence: 100%|████████████████| 153/153 [00:54<00:00,  2.81it/s]
Processing working/train_detected_faces\pins_Jeremy Renner: 100%|████████████████████| 129/129 [00:51<00:00,  2.49it/s]
Processing working/train_detected_faces\pins_Jessica Barden: 100%|███████████████████| 114/114 [00:47<00:00,  2.38it/s]
Processing working/train_detected_faces\pins_Jimmy Fallon: 100%|███████████████████████| 94/94 [00:34<00:00,  2.73it/s]
Processing working/train_detected_faces\pins_Johnny Depp: 100%|██████████████████████| 142/142 [00:47<00:00,  3.01it/s]
Processing working/train_detected_faces\pins_Josh Radnor: 100%|██████████████████████| 101/101 [00:34<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Katharine Mcphee: 100%|█████████████████| 159/159 [00:53<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Katherine Langford: 100%|███████████████| 196/196 [01:06<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Keanu Reeves: 100%|█████████████████████| 128/128 [00:42<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_kiernen shipka: 100%|███████████████████| 170/170 [00:56<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_Krysten Ritter: 100%|███████████████████| 142/142 [00:47<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Leonardo DiCaprio: 100%|████████████████| 173/173 [00:57<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Lili Reinhart: 100%|████████████████████| 108/108 [00:36<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Lindsey Morgan: 100%|███████████████████| 133/133 [00:45<00:00,  2.95it/s]
Processing working/train_detected_faces\pins_Lionel Messi: 100%|███████████████████████| 66/66 [00:22<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Logan Lerman: 100%|█████████████████████| 174/174 [00:58<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Madelaine Petsch: 100%|█████████████████| 134/134 [00:45<00:00,  2.95it/s]
Processing working/train_detected_faces\pins_Maisie Williams: 100%|██████████████████| 154/154 [00:51<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_margot robbie: 100%|████████████████████| 148/148 [00:49<00:00,  3.01it/s]
Processing working/train_detected_faces\pins_Maria Pedraza: 100%|██████████████████████| 78/78 [00:26<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_Marie Avgeropoulos: 100%|███████████████| 136/136 [00:46<00:00,  2.92it/s]
Processing working/train_detected_faces\pins_Mark Ruffalo: 100%|█████████████████████| 135/135 [00:47<00:00,  2.83it/s]
Processing working/train_detected_faces\pins_Mark Zuckerberg: 100%|████████████████████| 81/81 [00:27<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Megan Fox: 100%|████████████████████████| 173/173 [00:59<00:00,  2.93it/s]
Processing working/train_detected_faces\pins_melissa fumero: 100%|███████████████████| 137/137 [00:47<00:00,  2.87it/s]
Processing working/train_detected_faces\pins_Miley Cyrus: 100%|██████████████████████| 101/101 [00:33<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Millie Bobby Brown: 100%|███████████████| 139/139 [00:46<00:00,  2.98it/s]
Processing working/train_detected_faces\pins_Morena Baccarin: 100%|██████████████████| 161/161 [00:53<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Morgan Freeman: 100%|█████████████████████| 57/57 [00:19<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Nadia Hilker: 100%|███████████████████████| 94/94 [00:32<00:00,  2.92it/s]
Processing working/train_detected_faces\pins_Natalie Dormer: 100%|███████████████████| 144/144 [00:48<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Natalie Portman: 100%|██████████████████| 124/124 [00:41<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_Neil Patrick Harris: 100%|████████████████| 98/98 [00:33<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Pedro Alonso: 100%|█████████████████████| 100/100 [00:33<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Penn Badgley: 100%|█████████████████████| 142/142 [00:47<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_Rami Malek: 100%|███████████████████████| 124/124 [00:41<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Rebecca Ferguson: 100%|█████████████████| 155/155 [01:12<00:00,  2.13it/s]
Processing working/train_detected_faces\pins_Richard Harmon: 100%|█████████████████████| 98/98 [00:40<00:00,  2.41it/s]
Processing working/train_detected_faces\pins_Rihanna: 100%|████████████████████████████| 89/89 [00:30<00:00,  2.91it/s]
Processing working/train_detected_faces\pins_Robert De Niro: 100%|███████████████████| 115/115 [00:38<00:00,  2.95it/s]
Processing working/train_detected_faces\pins_Robert Downey Jr: 100%|█████████████████| 167/167 [00:56<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Sarah Wayne Callies: 100%|██████████████| 133/133 [00:44<00:00,  2.98it/s]
Processing working/train_detected_faces\pins_scarlett johansson: 100%|███████████████| 160/160 [00:54<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Selena Gomez: 100%|█████████████████████| 125/125 [00:43<00:00,  2.89it/s]
Processing working/train_detected_faces\pins_Shakira Isabel Mebarak: 100%|███████████| 126/126 [00:42<00:00,  2.94it/s]
Processing working/train_detected_faces\pins_Sophie Turner: 100%|████████████████████| 159/159 [00:53<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Stephen Amell: 100%|████████████████████| 129/129 [00:43<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Taylor Swift: 100%|███████████████████████| 43/43 [00:14<00:00,  3.04it/s]
Processing working/train_detected_faces\pins_Tom Cruise: 100%|███████████████████████| 151/151 [00:50<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_tom ellis: 100%|████████████████████████| 118/118 [00:39<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Tom Hardy: 100%|████████████████████████| 121/121 [00:40<00:00,  3.00it/s]
Processing working/train_detected_faces\pins_Tom Hiddleston: 100%|███████████████████| 137/137 [00:46<00:00,  2.97it/s]
Processing working/train_detected_faces\pins_Tom Holland: 100%|██████████████████████| 151/151 [00:50<00:00,  2.96it/s]
Processing working/train_detected_faces\pins_Tuppence Middleton: 100%|███████████████| 111/111 [00:36<00:00,  3.01it/s]
Processing working/train_detected_faces\pins_Ursula Corbero: 100%|█████████████████████| 86/86 [00:29<00:00,  2.95it/s]
Processing working/train_detected_faces\pins_Wentworth Miller: 100%|█████████████████| 137/137 [00:45<00:00,  2.98it/s]
Processing working/train_detected_faces\pins_Zac Efron: 100%|████████████████████████| 146/146 [00:48<00:00,  2.99it/s]
Processing working/train_detected_faces\pins_Zendaya: 100%|██████████████████████████| 105/105 [00:35<00:00,  2.98it/s]
Processing working/train_detected_faces\pins_Zoe Saldana: 100%|██████████████████████| 161/161 [00:54<00:00,  2.94it/s]

Embeddings extraction complete.

import os

# Directory path
output_directory = 'working/extracted_embeddings'

# Get folder names and file counts
folders_and_counts = [(folder, len(os.listdir(os.path.join(output_directory, folder)))) for folder in os.listdir(output_directory)]

# Print results
for folder, count in folders_and_counts:
    print(f"Folder: {folder}, Number of Files: {count}")

Folder: pins_Adriana Lima, Number of Files: 372
Folder: pins_Alex Lawther, Number of Files: 172
Folder: pins_Alexandra Daddario, Number of Files: 412
Folder: pins_Alvaro Morte, Number of Files: 188
Folder: pins_alycia dabnem carey, Number of Files: 314
Folder: pins_Amanda Crew, Number of Files: 220
Folder: pins_amber heard, Number of Files: 332
Folder: pins_Andy Samberg, Number of Files: 334
Folder: pins_Anne Hathaway, Number of Files: 314
Folder: pins_Anthony Mackie, Number of Files: 156
Folder: pins_Avril Lavigne, Number of Files: 216
Folder: pins_barack obama, Number of Files: 174
Folder: pins_barbara palvin, Number of Files: 288
Folder: pins_Ben Affleck, Number of Files: 196
Folder: pins_Bill Gates, Number of Files: 138
Folder: pins_Bobby Morley, Number of Files: 138
Folder: pins_Brenton Thwaites, Number of Files: 318
Folder: pins_Brian J. Smith, Number of Files: 156
Folder: pins_Brie Larson, Number of Files: 262
Folder: pins_camila mendes, Number of Files: 260
Folder: pins_Chris Evans, Number of Files: 222
Folder: pins_Chris Hemsworth, Number of Files: 266
Folder: pins_Chris Pratt, Number of Files: 316
Folder: pins_Christian Bale, Number of Files: 258
Folder: pins_Cristiano Ronaldo, Number of Files: 152
Folder: pins_Danielle Panabaker, Number of Files: 298
Folder: pins_Dominic Purcell, Number of Files: 216
Folder: pins_Dwayne Johnson, Number of Files: 156
Folder: pins_Eliza Taylor, Number of Files: 260
Folder: pins_Elizabeth Lail, Number of Files: 258
Folder: pins_elizabeth olsen, Number of Files: 388
Folder: pins_ellen page, Number of Files: 314
Folder: pins_elon musk, Number of Files: 206
Folder: pins_Emilia Clarke, Number of Files: 318
Folder: pins_Emma Stone, Number of Files: 200
Folder: pins_Emma Watson, Number of Files: 324
Folder: pins_gal gadot, Number of Files: 268
Folder: pins_grant gustin, Number of Files: 296
Folder: pins_Gwyneth Paltrow, Number of Files: 320
Folder: pins_Henry Cavil, Number of Files: 288
Folder: pins_Hugh Jackman, Number of Files: 288
Folder: pins_Inbar Lavi, Number of Files: 220
Folder: pins_Irina Shayk, Number of Files: 274
Folder: pins_Jake Mcdorman, Number of Files: 294
Folder: pins_Jason Momoa, Number of Files: 286
Folder: pins_jeff bezos, Number of Files: 154
Folder: pins_Jennifer Lawrence, Number of Files: 306
Folder: pins_Jeremy Renner, Number of Files: 258
Folder: pins_Jessica Barden, Number of Files: 228
Folder: pins_Jimmy Fallon, Number of Files: 188
Folder: pins_Johnny Depp, Number of Files: 284
Folder: pins_Josh Radnor, Number of Files: 202
Folder: pins_Katharine Mcphee, Number of Files: 318
Folder: pins_Katherine Langford, Number of Files: 392
Folder: pins_Keanu Reeves, Number of Files: 256
Folder: pins_kiernen shipka, Number of Files: 340
Folder: pins_Krysten Ritter, Number of Files: 284
Folder: pins_Leonardo DiCaprio, Number of Files: 346
Folder: pins_Lili Reinhart, Number of Files: 216
Folder: pins_Lindsey Morgan, Number of Files: 266
Folder: pins_Lionel Messi, Number of Files: 132
Folder: pins_Logan Lerman, Number of Files: 348
Folder: pins_Madelaine Petsch, Number of Files: 268
Folder: pins_Maisie Williams, Number of Files: 308
Folder: pins_margot robbie, Number of Files: 296
Folder: pins_Maria Pedraza, Number of Files: 156
Folder: pins_Marie Avgeropoulos, Number of Files: 272
Folder: pins_Mark Ruffalo, Number of Files: 270
Folder: pins_Mark Zuckerberg, Number of Files: 162
Folder: pins_Megan Fox, Number of Files: 346
Folder: pins_melissa fumero, Number of Files: 274
Folder: pins_Miley Cyrus, Number of Files: 202
Folder: pins_Millie Bobby Brown, Number of Files: 278
Folder: pins_Morena Baccarin, Number of Files: 322
Folder: pins_Morgan Freeman, Number of Files: 114
Folder: pins_Nadia Hilker, Number of Files: 188
Folder: pins_Natalie Dormer, Number of Files: 288
Folder: pins_Natalie Portman, Number of Files: 248
Folder: pins_Neil Patrick Harris, Number of Files: 196
Folder: pins_Pedro Alonso, Number of Files: 200
Folder: pins_Penn Badgley, Number of Files: 284
Folder: pins_Rami Malek, Number of Files: 248
Folder: pins_Rebecca Ferguson, Number of Files: 310
Folder: pins_Richard Harmon, Number of Files: 196
Folder: pins_Rihanna, Number of Files: 178
Folder: pins_Robert De Niro, Number of Files: 230
Folder: pins_Robert Downey Jr, Number of Files: 334
Folder: pins_Sarah Wayne Callies, Number of Files: 266
Folder: pins_scarlett johansson, Number of Files: 320
Folder: pins_Selena Gomez, Number of Files: 250
Folder: pins_Shakira Isabel Mebarak, Number of Files: 252
Folder: pins_Sophie Turner, Number of Files: 318
Folder: pins_Stephen Amell, Number of Files: 258
Folder: pins_Taylor Swift, Number of Files: 86
Folder: pins_Tom Cruise, Number of Files: 302
Folder: pins_tom ellis, Number of Files: 236
Folder: pins_Tom Hardy, Number of Files: 242
Folder: pins_Tom Hiddleston, Number of Files: 274
Folder: pins_Tom Holland, Number of Files: 302
Folder: pins_Tuppence Middleton, Number of Files: 222
Folder: pins_Ursula Corbero, Number of Files: 172
Folder: pins_Wentworth Miller, Number of Files: 274
Folder: pins_Zac Efron, Number of Files: 292
Folder: pins_Zendaya, Number of Files: 210
Folder: pins_Zoe Saldana, Number of Files: 322

import shutil

folder_path = 'working/extracted_embeddings'

# Check if the folder exists before deleting
if os.path.exists(folder_path):
    shutil.rmtree(folder_path)
    print(f"The folder {folder_path} has been deleted.")
else:
    print(f"The folder {folder_path} does not exist.")

The folder working/extracted_embeddings has been deleted.

import shutil

source_folder = "working/extracted_embeddings"
destination_folder = "working/extracted_embeddingstest"

# Create the destination folder if it doesn't exist
os.makedirs(destination_folder, exist_ok=True)

# Iterate through each folder in the source directory
for folder_name in os.listdir(source_folder):
    folder_path = os.path.join(source_folder, folder_name)

    # Check if it's a directory
    if os.path.isdir(folder_path):
        # Create the corresponding folder in the destination directory
        destination_folder_path = os.path.join(destination_folder, folder_name)
        os.makedirs(destination_folder_path, exist_ok=True)

        # Get the list of files in the current folder
        files = os.listdir(folder_path)

        # Move the first two files that do not contain "augmented" in their names
        moved_files = 0
        for file_name in files:
            if "augmented" not in file_name:
                source_file_path = os.path.join(folder_path, file_name)
                destination_file_path = os.path.join(destination_folder_path, file_name)

                # Move the file (not copy)
                shutil.move(source_file_path, destination_file_path)
                moved_files += 1

                if moved_files == 2:
                    break  # Break after moving two suitable files

print("Files moved successfully.")

Files moved successfully.

#!pip install face_recognition

#!pip install facenet-pytorch numpy scikit-learn

from sklearn.metrics import pairwise_distances

# Function to calculate average embedding for each label
def calculate_average_embeddings(base_folder):
    average_embeddings = {}

    for label_folder in os.listdir(base_folder):
        label_path = os.path.join(base_folder, label_folder)
        if os.path.isdir(label_path):
            label_embeddings = []
            for file_name in os.listdir(label_path):
                file_path = os.path.join(label_path, file_name)
                # Load embeddings using your preferred method (e.g., np.load())
                embedding = np.load(file_path)
                label_embeddings.append(embedding)

            # Calculate average embedding for the label
            average_embedding = np.mean(label_embeddings, axis=0)
            average_embeddings[label_folder] = average_embedding

    return average_embeddings

# Function to recognize faces based on existing embeddings
def recognize_faces(test_folder, average_embeddings):
    predictions = []

    for label_folder in os.listdir(test_folder):
        label_path = os.path.join(test_folder, label_folder)
        if os.path.isdir(label_path):
            for file_name in os.listdir(label_path):
                file_path = os.path.join(label_path, file_name)

                # Load test embedding from the saved numpy file
                test_embedding = np.load(file_path)

                # Compare test embedding with averaged embeddings
                distances = {}
                for label, avg_embedding in average_embeddings.items():
                    distance = np.linalg.norm(test_embedding - avg_embedding)
                    distances[label] = distance

                # Predict the label with the minimum distance
                predicted_label = min(distances, key=distances.get)
                predictions.append((file_name, label_folder, predicted_label))

    return predictions


# folder paths
base_folder = 'working/extracted_embeddings'
test_folder = 'working/extracted_embeddingstest'

# Load existing average embeddings
average_embeddings = calculate_average_embeddings(base_folder)

# Recognize faces in the test folder
predictions = recognize_faces(test_folder, average_embeddings)

# Print the predictions
for file_name, actual_label, predicted_label in predictions:
    print(f"{file_name}: Actual Label - {actual_label}, Predicted Label - {predicted_label}")

pins_Adriana Lima_detected_face_0_embedding.npy: Actual Label - pins_Adriana Lima, Predicted Label - pins_Adriana Lima
pins_Adriana Lima_detected_face_101_embedding.npy: Actual Label - pins_Adriana Lima, Predicted Label - pins_Adriana Lima
pins_Alex Lawther_detected_face_214_embedding.npy: Actual Label - pins_Alex Lawther, Predicted Label - pins_Alex Lawther
pins_Alex Lawther_detected_face_215_embedding.npy: Actual Label - pins_Alex Lawther, Predicted Label - pins_Alex Lawther
pins_Alexandra Daddario_detected_face_365_embedding.npy: Actual Label - pins_Alexandra Daddario, Predicted Label - pins_Alexandra Daddario
pins_Alexandra Daddario_detected_face_366_embedding.npy: Actual Label - pins_Alexandra Daddario, Predicted Label - pins_Alexandra Daddario
pins_Alvaro Morte_detected_face_590_embedding.npy: Actual Label - pins_Alvaro Morte, Predicted Label - pins_Alvaro Morte
pins_Alvaro Morte_detected_face_591_embedding.npy: Actual Label - pins_Alvaro Morte, Predicted Label - pins_Alvaro Morte
pins_alycia dabnem carey_detected_face_729_embedding.npy: Actual Label - pins_alycia dabnem carey, Predicted Label - pins_alycia dabnem carey
pins_alycia dabnem carey_detected_face_730_embedding.npy: Actual Label - pins_alycia dabnem carey, Predicted Label - pins_alycia dabnem carey
pins_Amanda Crew_detected_face_1000_embedding.npy: Actual Label - pins_Amanda Crew, Predicted Label - pins_Amanda Crew
pins_Amanda Crew_detected_face_1001_embedding.npy: Actual Label - pins_Amanda Crew, Predicted Label - pins_Amanda Crew
pins_amber heard_detected_face_1057_embedding.npy: Actual Label - pins_amber heard, Predicted Label - pins_amber heard
pins_amber heard_detected_face_1058_embedding.npy: Actual Label - pins_amber heard, Predicted Label - pins_amber heard
pins_Andy Samberg_detected_face_1275_embedding.npy: Actual Label - pins_Andy Samberg, Predicted Label - pins_Andy Samberg
pins_Andy Samberg_detected_face_1276_embedding.npy: Actual Label - pins_Andy Samberg, Predicted Label - pins_Andy Samberg
pins_Anne Hathaway_detected_face_1471_embedding.npy: Actual Label - pins_Anne Hathaway, Predicted Label - pins_Anne Hathaway
pins_Anne Hathaway_detected_face_1472_embedding.npy: Actual Label - pins_Anne Hathaway, Predicted Label - pins_Anne Hathaway
pins_Anthony Mackie_detected_face_1675_embedding.npy: Actual Label - pins_Anthony Mackie, Predicted Label - pins_Anthony Mackie
pins_Anthony Mackie_detected_face_1676_embedding.npy: Actual Label - pins_Anthony Mackie, Predicted Label - pins_Anthony Mackie
pins_Avril Lavigne_detected_face_1798_embedding.npy: Actual Label - pins_Avril Lavigne, Predicted Label - pins_Avril Lavigne
pins_Avril Lavigne_detected_face_1799_embedding.npy: Actual Label - pins_Avril Lavigne, Predicted Label - pins_Avril Lavigne
pins_barack obama_detected_face_1960_embedding.npy: Actual Label - pins_barack obama, Predicted Label - pins_barack obama
pins_barack obama_detected_face_1962_embedding.npy: Actual Label - pins_barack obama, Predicted Label - pins_barack obama
pins_barbara palvin_detected_face_2079_embedding.npy: Actual Label - pins_barbara palvin, Predicted Label - pins_barbara palvin
pins_barbara palvin_detected_face_2080_embedding.npy: Actual Label - pins_barbara palvin, Predicted Label - pins_barbara palvin
pins_Ben Affleck_detected_face_2276_embedding.npy: Actual Label - pins_Ben Affleck, Predicted Label - pins_Ben Affleck
pins_Ben Affleck_detected_face_2278_embedding.npy: Actual Label - pins_Ben Affleck, Predicted Label - pins_Ben Affleck
pins_Bill Gates_detected_face_2404_embedding.npy: Actual Label - pins_Bill Gates, Predicted Label - pins_Bill Gates
pins_Bill Gates_detected_face_2405_embedding.npy: Actual Label - pins_Bill Gates, Predicted Label - pins_Bill Gates
pins_Bobby Morley_detected_face_2524_embedding.npy: Actual Label - pins_Bobby Morley, Predicted Label - pins_Bobby Morley
pins_Bobby Morley_detected_face_2526_embedding.npy: Actual Label - pins_Bobby Morley, Predicted Label - pins_Bobby Morley
pins_Brenton Thwaites_detected_face_2662_embedding.npy: Actual Label - pins_Brenton Thwaites, Predicted Label - pins_Johnny Depp
pins_Brenton Thwaites_detected_face_2663_embedding.npy: Actual Label - pins_Brenton Thwaites, Predicted Label - pins_Brenton Thwaites
pins_Brian J. Smith_detected_face_2871_embedding.npy: Actual Label - pins_Brian J. Smith, Predicted Label - pins_Taylor Swift
pins_Brian J. Smith_detected_face_2873_embedding.npy: Actual Label - pins_Brian J. Smith, Predicted Label - pins_Brian J. Smith
pins_Brie Larson_detected_face_2973_embedding.npy: Actual Label - pins_Brie Larson, Predicted Label - pins_Brie Larson
pins_Brie Larson_detected_face_2974_embedding.npy: Actual Label - pins_Brie Larson, Predicted Label - pins_Brie Larson
pins_camila mendes_detected_face_3142_embedding.npy: Actual Label - pins_camila mendes, Predicted Label - pins_camila mendes
pins_camila mendes_detected_face_3143_embedding.npy: Actual Label - pins_camila mendes, Predicted Label - pins_camila mendes
pins_Chris Evans_detected_face_3304_embedding.npy: Actual Label - pins_Chris Evans, Predicted Label - pins_Chris Evans
pins_Chris Evans_detected_face_3305_embedding.npy: Actual Label - pins_Chris Evans, Predicted Label - pins_Chris Evans
pins_Chris Hemsworth_detected_face_3471_embedding.npy: Actual Label - pins_Chris Hemsworth, Predicted Label - pins_Chris Hemsworth
pins_Chris Hemsworth_detected_face_3472_embedding.npy: Actual Label - pins_Chris Hemsworth, Predicted Label - pins_Chris Hemsworth
pins_Chris Pratt_detected_face_3629_embedding.npy: Actual Label - pins_Chris Pratt, Predicted Label - pins_Chris Pratt
pins_Chris Pratt_detected_face_3630_embedding.npy: Actual Label - pins_Chris Pratt, Predicted Label - pins_Chris Pratt
pins_Christian Bale_detected_face_3805_embedding.npy: Actual Label - pins_Christian Bale, Predicted Label - pins_Johnny Depp
pins_Christian Bale_detected_face_3806_embedding.npy: Actual Label - pins_Christian Bale, Predicted Label - pins_Christian Bale
pins_Cristiano Ronaldo_detected_face_3959_embedding.npy: Actual Label - pins_Cristiano Ronaldo, Predicted Label - pins_Cristiano Ronaldo
pins_Cristiano Ronaldo_detected_face_3960_embedding.npy: Actual Label - pins_Cristiano Ronaldo, Predicted Label - pins_Cristiano Ronaldo
pins_Danielle Panabaker_detected_face_4057_embedding.npy: Actual Label - pins_Danielle Panabaker, Predicted Label - pins_Danielle Panabaker
pins_Danielle Panabaker_detected_face_4058_embedding.npy: Actual Label - pins_Danielle Panabaker, Predicted Label - pins_Elizabeth Lail
pins_Dominic Purcell_detected_face_4239_embedding.npy: Actual Label - pins_Dominic Purcell, Predicted Label - pins_Dominic Purcell
pins_Dominic Purcell_detected_face_4240_embedding.npy: Actual Label - pins_Dominic Purcell, Predicted Label - pins_Dominic Purcell
pins_Dwayne Johnson_detected_face_4385_embedding.npy: Actual Label - pins_Dwayne Johnson, Predicted Label - pins_Dwayne Johnson
pins_Dwayne Johnson_detected_face_4389_embedding.npy: Actual Label - pins_Dwayne Johnson, Predicted Label - pins_Dwayne Johnson
pins_Eliza Taylor_detected_face_4525_embedding.npy: Actual Label - pins_Eliza Taylor, Predicted Label - pins_Eliza Taylor
pins_Eliza Taylor_detected_face_4527_embedding.npy: Actual Label - pins_Eliza Taylor, Predicted Label - pins_Eliza Taylor
pins_Elizabeth Lail_detected_face_4687_embedding.npy: Actual Label - pins_Elizabeth Lail, Predicted Label - pins_Elizabeth Lail
pins_Elizabeth Lail_detected_face_4688_embedding.npy: Actual Label - pins_Elizabeth Lail, Predicted Label - pins_Elizabeth Lail
pins_elizabeth olsen_detected_face_4846_embedding.npy: Actual Label - pins_elizabeth olsen, Predicted Label - pins_elizabeth olsen
pins_elizabeth olsen_detected_face_4847_embedding.npy: Actual Label - pins_elizabeth olsen, Predicted Label - pins_elizabeth olsen
pins_ellen page_detected_face_5066_embedding.npy: Actual Label - pins_ellen page, Predicted Label - pins_ellen page
pins_ellen page_detected_face_5068_embedding.npy: Actual Label - pins_ellen page, Predicted Label - pins_ellen page
pins_elon musk_detected_face_5255_embedding.npy: Actual Label - pins_elon musk, Predicted Label - pins_elon musk
pins_elon musk_detected_face_5256_embedding.npy: Actual Label - pins_elon musk, Predicted Label - pins_elon musk
pins_Emilia Clarke_detected_face_5389_embedding.npy: Actual Label - pins_Emilia Clarke, Predicted Label - pins_Emilia Clarke
pins_Emilia Clarke_detected_face_5391_embedding.npy: Actual Label - pins_Emilia Clarke, Predicted Label - pins_Emilia Clarke
pins_Emma Stone_detected_face_5600_embedding.npy: Actual Label - pins_Emma Stone, Predicted Label - pins_Emma Stone
pins_Emma Stone_detected_face_5601_embedding.npy: Actual Label - pins_Emma Stone, Predicted Label - pins_Emma Stone
pins_Emma Watson_detected_face_5738_embedding.npy: Actual Label - pins_Emma Watson, Predicted Label - pins_Emma Watson
pins_Emma Watson_detected_face_5739_embedding.npy: Actual Label - pins_Emma Watson, Predicted Label - pins_Emma Watson
pins_gal gadot_detected_face_5950_embedding.npy: Actual Label - pins_gal gadot, Predicted Label - pins_gal gadot
pins_gal gadot_detected_face_5951_embedding.npy: Actual Label - pins_gal gadot, Predicted Label - pins_gal gadot
pins_grant gustin_detected_face_6148_embedding.npy: Actual Label - pins_grant gustin, Predicted Label - pins_grant gustin
pins_grant gustin_detected_face_6149_embedding.npy: Actual Label - pins_grant gustin, Predicted Label - pins_grant gustin
pins_Gwyneth Paltrow_detected_face_6331_embedding.npy: Actual Label - pins_Gwyneth Paltrow, Predicted Label - pins_Gwyneth Paltrow
pins_Gwyneth Paltrow_detected_face_6332_embedding.npy: Actual Label - pins_Gwyneth Paltrow, Predicted Label - pins_Gwyneth Paltrow
pins_Henry Cavil_detected_face_6519_embedding.npy: Actual Label - pins_Henry Cavil, Predicted Label - pins_Mark Ruffalo
pins_Henry Cavil_detected_face_6520_embedding.npy: Actual Label - pins_Henry Cavil, Predicted Label - pins_Johnny Depp
pins_Hugh Jackman_detected_face_6713_embedding.npy: Actual Label - pins_Hugh Jackman, Predicted Label - pins_Hugh Jackman
pins_Hugh Jackman_detected_face_6714_embedding.npy: Actual Label - pins_Hugh Jackman, Predicted Label - pins_Hugh Jackman
pins_Inbar Lavi_detected_face_6892_embedding.npy: Actual Label - pins_Inbar Lavi, Predicted Label - pins_Inbar Lavi
pins_Inbar Lavi_detected_face_6893_embedding.npy: Actual Label - pins_Inbar Lavi, Predicted Label - pins_Inbar Lavi
pins_Irina Shayk_detected_face_7019_embedding.npy: Actual Label - pins_Irina Shayk, Predicted Label - pins_Irina Shayk
pins_Irina Shayk_detected_face_7022_embedding.npy: Actual Label - pins_Irina Shayk, Predicted Label - pins_Irina Shayk
pins_Jake Mcdorman_detected_face_7175_embedding.npy: Actual Label - pins_Jake Mcdorman, Predicted Label - pins_Jake Mcdorman
pins_Jake Mcdorman_detected_face_7176_embedding.npy: Actual Label - pins_Jake Mcdorman, Predicted Label - pins_Jake Mcdorman
pins_Jason Momoa_detected_face_7334_embedding.npy: Actual Label - pins_Jason Momoa, Predicted Label - pins_Jason Momoa
pins_Jason Momoa_detected_face_7335_embedding.npy: Actual Label - pins_Jason Momoa, Predicted Label - pins_Jason Momoa
pins_jeff bezos_detected_face_7518_embedding.npy: Actual Label - pins_jeff bezos, Predicted Label - pins_jeff bezos
pins_jeff bezos_detected_face_7519_embedding.npy: Actual Label - pins_jeff bezos, Predicted Label - pins_jeff bezos
pins_Jennifer Lawrence_detected_face_7624_embedding.npy: Actual Label - pins_Jennifer Lawrence, Predicted Label - pins_Jennifer Lawrence
pins_Jennifer Lawrence_detected_face_7625_embedding.npy: Actual Label - pins_Jennifer Lawrence, Predicted Label - pins_Jennifer Lawrence
pins_Jeremy Renner_detected_face_7804_embedding.npy: Actual Label - pins_Jeremy Renner, Predicted Label - pins_Jeremy Renner
pins_Jeremy Renner_detected_face_7805_embedding.npy: Actual Label - pins_Jeremy Renner, Predicted Label - pins_Jeremy Renner
pins_Jessica Barden_detected_face_7971_embedding.npy: Actual Label - pins_Jessica Barden, Predicted Label - pins_Jessica Barden
pins_Jessica Barden_detected_face_7972_embedding.npy: Actual Label - pins_Jessica Barden, Predicted Label - pins_Jessica Barden
pins_Jimmy Fallon_detected_face_8112_embedding.npy: Actual Label - pins_Jimmy Fallon, Predicted Label - pins_Jimmy Fallon
pins_Jimmy Fallon_detected_face_8113_embedding.npy: Actual Label - pins_Jimmy Fallon, Predicted Label - pins_Jimmy Fallon
pins_Johnny Depp_detected_face_8226_embedding.npy: Actual Label - pins_Johnny Depp, Predicted Label - pins_Johnny Depp
pins_Johnny Depp_detected_face_8227_embedding.npy: Actual Label - pins_Johnny Depp, Predicted Label - pins_Johnny Depp
pins_Josh Radnor_detected_face_8407_embedding.npy: Actual Label - pins_Josh Radnor, Predicted Label - pins_Josh Radnor
pins_Josh Radnor_detected_face_8408_embedding.npy: Actual Label - pins_Josh Radnor, Predicted Label - pins_Josh Radnor
pins_Katharine Mcphee_detected_face_8524_embedding.npy: Actual Label - pins_Katharine Mcphee, Predicted Label - pins_Katharine Mcphee
pins_Katharine Mcphee_detected_face_8525_embedding.npy: Actual Label - pins_Katharine Mcphee, Predicted Label - pins_Katharine Mcphee
pins_Katherine Langford_detected_face_8701_embedding.npy: Actual Label - pins_Katherine Langford, Predicted Label - pins_ellen page
pins_Katherine Langford_detected_face_8702_embedding.npy: Actual Label - pins_Katherine Langford, Predicted Label - pins_Katherine Langford
pins_Keanu Reeves_detected_face_8927_embedding.npy: Actual Label - pins_Keanu Reeves, Predicted Label - pins_Keanu Reeves
pins_Keanu Reeves_detected_face_8928_embedding.npy: Actual Label - pins_Keanu Reeves, Predicted Label - pins_Keanu Reeves
pins_kiernen shipka_detected_face_9087_embedding.npy: Actual Label - pins_kiernen shipka, Predicted Label - pins_kiernen shipka
pins_kiernen shipka_detected_face_9088_embedding.npy: Actual Label - pins_kiernen shipka, Predicted Label - pins_kiernen shipka
pins_Krysten Ritter_detected_face_9290_embedding.npy: Actual Label - pins_Krysten Ritter, Predicted Label - pins_Krysten Ritter
pins_Krysten Ritter_detected_face_9291_embedding.npy: Actual Label - pins_Krysten Ritter, Predicted Label - pins_Krysten Ritter
pins_Leonardo DiCaprio_detected_face_9461_embedding.npy: Actual Label - pins_Leonardo DiCaprio, Predicted Label - pins_Leonardo DiCaprio
pins_Leonardo DiCaprio_detected_face_9462_embedding.npy: Actual Label - pins_Leonardo DiCaprio, Predicted Label - pins_Leonardo DiCaprio
pins_Lili Reinhart_detected_face_9698_embedding.npy: Actual Label - pins_Lili Reinhart, Predicted Label - pins_elizabeth olsen
pins_Lili Reinhart_detected_face_9699_embedding.npy: Actual Label - pins_Lili Reinhart, Predicted Label - pins_Lili Reinhart
pins_Lindsey Morgan_detected_face_10001_embedding.npy: Actual Label - pins_Lindsey Morgan, Predicted Label - pins_Lindsey Morgan
pins_Lindsey Morgan_detected_face_10002_embedding.npy: Actual Label - pins_Lindsey Morgan, Predicted Label - pins_Lindsey Morgan
pins_Lionel Messi_detected_face_10017_embedding.npy: Actual Label - pins_Lionel Messi, Predicted Label - pins_Lionel Messi
pins_Lionel Messi_detected_face_10018_embedding.npy: Actual Label - pins_Lionel Messi, Predicted Label - pins_Lionel Messi
pins_Logan Lerman_detected_face_10103_embedding.npy: Actual Label - pins_Logan Lerman, Predicted Label - pins_Logan Lerman
pins_Logan Lerman_detected_face_10104_embedding.npy: Actual Label - pins_Logan Lerman, Predicted Label - pins_Logan Lerman
pins_Madelaine Petsch_detected_face_10316_embedding.npy: Actual Label - pins_Madelaine Petsch, Predicted Label - pins_Madelaine Petsch
pins_Madelaine Petsch_detected_face_10317_embedding.npy: Actual Label - pins_Madelaine Petsch, Predicted Label - pins_Madelaine Petsch
pins_Maisie Williams_detected_face_10509_embedding.npy: Actual Label - pins_Maisie Williams, Predicted Label - pins_Maisie Williams
pins_Maisie Williams_detected_face_10511_embedding.npy: Actual Label - pins_Maisie Williams, Predicted Label - pins_Maisie Williams
pins_margot robbie_detected_face_10700_embedding.npy: Actual Label - pins_margot robbie, Predicted Label - pins_margot robbie
pins_margot robbie_detected_face_10703_embedding.npy: Actual Label - pins_margot robbie, Predicted Label - pins_margot robbie
pins_Maria Pedraza_detected_face_10921_embedding.npy: Actual Label - pins_Maria Pedraza, Predicted Label - pins_Emilia Clarke
pins_Maria Pedraza_detected_face_10922_embedding.npy: Actual Label - pins_Maria Pedraza, Predicted Label - pins_Maria Pedraza
pins_Marie Avgeropoulos_detected_face_11043_embedding.npy: Actual Label - pins_Marie Avgeropoulos, Predicted Label - pins_Marie Avgeropoulos
pins_Marie Avgeropoulos_detected_face_11044_embedding.npy: Actual Label - pins_Marie Avgeropoulos, Predicted Label - pins_Marie Avgeropoulos
pins_Mark Ruffalo_detected_face_11205_embedding.npy: Actual Label - pins_Mark Ruffalo, Predicted Label - pins_Alex Lawther
pins_Mark Ruffalo_detected_face_11207_embedding.npy: Actual Label - pins_Mark Ruffalo, Predicted Label - pins_Mark Ruffalo
pins_Mark Zuckerberg_detected_face_11382_embedding.npy: Actual Label - pins_Mark Zuckerberg, Predicted Label - pins_Mark Zuckerberg
pins_Mark Zuckerberg_detected_face_11383_embedding.npy: Actual Label - pins_Mark Zuckerberg, Predicted Label - pins_Mark Zuckerberg
pins_Megan Fox_detected_face_11477_embedding.npy: Actual Label - pins_Megan Fox, Predicted Label - pins_Megan Fox
pins_Megan Fox_detected_face_11479_embedding.npy: Actual Label - pins_Megan Fox, Predicted Label - pins_Megan Fox
pins_melissa fumero_detected_face_11686_embedding.npy: Actual Label - pins_melissa fumero, Predicted Label - pins_melissa fumero
pins_melissa fumero_detected_face_11687_embedding.npy: Actual Label - pins_melissa fumero, Predicted Label - pins_melissa fumero
pins_Miley Cyrus_detected_face_11842_embedding.npy: Actual Label - pins_Miley Cyrus, Predicted Label - pins_Rihanna
pins_Miley Cyrus_detected_face_11844_embedding.npy: Actual Label - pins_Miley Cyrus, Predicted Label - pins_Miley Cyrus
pins_Millie Bobby Brown_detected_face_12018_embedding.npy: Actual Label - pins_Millie Bobby Brown, Predicted Label - pins_Millie Bobby Brown
pins_Millie Bobby Brown_detected_face_12019_embedding.npy: Actual Label - pins_Millie Bobby Brown, Predicted Label - pins_Millie Bobby Brown
pins_Morena Baccarin_detected_face_12209_embedding.npy: Actual Label - pins_Morena Baccarin, Predicted Label - pins_Morena Baccarin
pins_Morena Baccarin_detected_face_12210_embedding.npy: Actual Label - pins_Morena Baccarin, Predicted Label - pins_Morena Baccarin
pins_Morgan Freeman_detected_face_12386_embedding.npy: Actual Label - pins_Morgan Freeman, Predicted Label - pins_Morgan Freeman
pins_Morgan Freeman_detected_face_12387_embedding.npy: Actual Label - pins_Morgan Freeman, Predicted Label - pins_Morgan Freeman
pins_Nadia Hilker_detected_face_12489_embedding.npy: Actual Label - pins_Nadia Hilker, Predicted Label - pins_Nadia Hilker
pins_Nadia Hilker_detected_face_12493_embedding.npy: Actual Label - pins_Nadia Hilker, Predicted Label - pins_Selena Gomez
pins_Natalie Dormer_detected_face_12623_embedding.npy: Actual Label - pins_Natalie Dormer, Predicted Label - pins_Brie Larson
pins_Natalie Dormer_detected_face_12624_embedding.npy: Actual Label - pins_Natalie Dormer, Predicted Label - pins_Natalie Dormer
pins_Natalie Portman_detected_face_12820_embedding.npy: Actual Label - pins_Natalie Portman, Predicted Label - pins_Natalie Portman
pins_Natalie Portman_detected_face_12821_embedding.npy: Actual Label - pins_Natalie Portman, Predicted Label - pins_Natalie Portman
pins_Neil Patrick Harris_detected_face_12986_embedding.npy: Actual Label - pins_Neil Patrick Harris, Predicted Label - pins_Neil Patrick Harris
pins_Neil Patrick Harris_detected_face_12987_embedding.npy: Actual Label - pins_Neil Patrick Harris, Predicted Label - pins_Robert De Niro
pins_Pedro Alonso_detected_face_13102_embedding.npy: Actual Label - pins_Pedro Alonso, Predicted Label - pins_Pedro Alonso
pins_Pedro Alonso_detected_face_13103_embedding.npy: Actual Label - pins_Pedro Alonso, Predicted Label - pins_Pedro Alonso
pins_Penn Badgley_detected_face_13228_embedding.npy: Actual Label - pins_Penn Badgley, Predicted Label - pins_Penn Badgley
pins_Penn Badgley_detected_face_13229_embedding.npy: Actual Label - pins_Penn Badgley, Predicted Label - pins_Penn Badgley
pins_Rami Malek_detected_face_13398_embedding.npy: Actual Label - pins_Rami Malek, Predicted Label - pins_Robert Downey Jr
pins_Rami Malek_detected_face_13400_embedding.npy: Actual Label - pins_Rami Malek, Predicted Label - pins_Rami Malek
pins_Rebecca Ferguson_detected_face_13558_embedding.npy: Actual Label - pins_Rebecca Ferguson, Predicted Label - pins_Rebecca Ferguson
pins_Rebecca Ferguson_detected_face_13561_embedding.npy: Actual Label - pins_Rebecca Ferguson, Predicted Label - pins_Rebecca Ferguson
pins_Richard Harmon_detected_face_13739_embedding.npy: Actual Label - pins_Richard Harmon, Predicted Label - pins_Richard Harmon
pins_Richard Harmon_detected_face_13741_embedding.npy: Actual Label - pins_Richard Harmon, Predicted Label - pins_Richard Harmon
pins_Rihanna_detected_face_13884_embedding.npy: Actual Label - pins_Rihanna, Predicted Label - pins_Rihanna
pins_Rihanna_detected_face_13885_embedding.npy: Actual Label - pins_Rihanna, Predicted Label - pins_Rihanna
pins_Robert De Niro_detected_face_14017_embedding.npy: Actual Label - pins_Robert De Niro, Predicted Label - pins_Robert De Niro
pins_Robert De Niro_detected_face_14018_embedding.npy: Actual Label - pins_Robert De Niro, Predicted Label - pins_Mark Ruffalo
pins_Robert Downey Jr_detected_face_14174_embedding.npy: Actual Label - pins_Robert Downey Jr, Predicted Label - pins_Robert Downey Jr
pins_Robert Downey Jr_detected_face_14175_embedding.npy: Actual Label - pins_Robert Downey Jr, Predicted Label - pins_Robert Downey Jr
pins_Sarah Wayne Callies_detected_face_14406_embedding.npy: Actual Label - pins_Sarah Wayne Callies, Predicted Label - pins_Sarah Wayne Callies
pins_Sarah Wayne Callies_detected_face_14407_embedding.npy: Actual Label - pins_Sarah Wayne Callies, Predicted Label - pins_Sarah Wayne Callies
pins_scarlett johansson_detected_face_14565_embedding.npy: Actual Label - pins_scarlett johansson, Predicted Label - pins_scarlett johansson
pins_scarlett johansson_detected_face_14568_embedding.npy: Actual Label - pins_scarlett johansson, Predicted Label - pins_scarlett johansson
pins_Selena Gomez_detected_face_14767_embedding.npy: Actual Label - pins_Selena Gomez, Predicted Label - pins_Selena Gomez
pins_Selena Gomez_detected_face_14768_embedding.npy: Actual Label - pins_Selena Gomez, Predicted Label - pins_Selena Gomez
pins_Shakira Isabel Mebarak_detected_face_14952_embedding.npy: Actual Label - pins_Shakira Isabel Mebarak, Predicted Label - pins_Shakira Isabel Mebarak
pins_Shakira Isabel Mebarak_detected_face_14953_embedding.npy: Actual Label - pins_Shakira Isabel Mebarak, Predicted Label - pins_Shakira Isabel Mebarak
pins_Sophie Turner_detected_face_15106_embedding.npy: Actual Label - pins_Sophie Turner, Predicted Label - pins_Sophie Turner
pins_Sophie Turner_detected_face_15107_embedding.npy: Actual Label - pins_Sophie Turner, Predicted Label - pins_Sophie Turner
pins_Stephen Amell_detected_face_15311_embedding.npy: Actual Label - pins_Stephen Amell, Predicted Label - pins_Stephen Amell
pins_Stephen Amell_detected_face_15312_embedding.npy: Actual Label - pins_Stephen Amell, Predicted Label - pins_Stephen Amell
pins_Taylor Swift_detected_face_15474_embedding.npy: Actual Label - pins_Taylor Swift, Predicted Label - pins_Taylor Swift
pins_Taylor Swift_detected_face_15478_embedding.npy: Actual Label - pins_Taylor Swift, Predicted Label - pins_Taylor Swift
pins_Tom Cruise_detected_face_15600_embedding.npy: Actual Label - pins_Tom Cruise, Predicted Label - pins_Tom Cruise
pins_Tom Cruise_detected_face_15601_embedding.npy: Actual Label - pins_Tom Cruise, Predicted Label - pins_Tom Cruise
pins_tom ellis_detected_face_15792_embedding.npy: Actual Label - pins_tom ellis, Predicted Label - pins_tom ellis
pins_tom ellis_detected_face_15794_embedding.npy: Actual Label - pins_tom ellis, Predicted Label - pins_tom ellis
pins_Tom Hardy_detected_face_15973_embedding.npy: Actual Label - pins_Tom Hardy, Predicted Label - pins_Tom Hardy
pins_Tom Hardy_detected_face_15974_embedding.npy: Actual Label - pins_Tom Hardy, Predicted Label - pins_Tom Hardy
pins_Tom Hiddleston_detected_face_16171_embedding.npy: Actual Label - pins_Tom Hiddleston, Predicted Label - pins_Tom Hiddleston
pins_Tom Hiddleston_detected_face_16172_embedding.npy: Actual Label - pins_Tom Hiddleston, Predicted Label - pins_Tom Hiddleston
pins_Tom Holland_detected_face_16351_embedding.npy: Actual Label - pins_Tom Holland, Predicted Label - pins_Tom Holland
pins_Tom Holland_detected_face_16352_embedding.npy: Actual Label - pins_Tom Holland, Predicted Label - pins_Tom Holland
pins_Tuppence Middleton_detected_face_16541_embedding.npy: Actual Label - pins_Tuppence Middleton, Predicted Label - pins_Tuppence Middleton
pins_Tuppence Middleton_detected_face_16543_embedding.npy: Actual Label - pins_Tuppence Middleton, Predicted Label - pins_Tuppence Middleton
pins_Ursula Corbero_detected_face_16677_embedding.npy: Actual Label - pins_Ursula Corbero, Predicted Label - pins_Ursula Corbero
pins_Ursula Corbero_detected_face_16678_embedding.npy: Actual Label - pins_Ursula Corbero, Predicted Label - pins_Ursula Corbero
pins_Wentworth Miller_detected_face_16840_embedding.npy: Actual Label - pins_Wentworth Miller, Predicted Label - pins_Wentworth Miller
pins_Wentworth Miller_detected_face_16841_embedding.npy: Actual Label - pins_Wentworth Miller, Predicted Label - pins_Wentworth Miller
pins_Zac Efron_detected_face_17019_embedding.npy: Actual Label - pins_Zac Efron, Predicted Label - pins_Zac Efron
pins_Zac Efron_detected_face_17020_embedding.npy: Actual Label - pins_Zac Efron, Predicted Label - pins_Zac Efron
pins_Zendaya_detected_face_17210_embedding.npy: Actual Label - pins_Zendaya, Predicted Label - pins_Zendaya
pins_Zendaya_detected_face_17211_embedding.npy: Actual Label - pins_Zendaya, Predicted Label - pins_Leonardo DiCaprio
pins_Zoe Saldana_detected_face_17348_embedding.npy: Actual Label - pins_Zoe Saldana, Predicted Label - pins_Zoe Saldana
pins_Zoe Saldana_detected_face_17349_embedding.npy: Actual Label - pins_Zoe Saldana, Predicted Label - pins_Zoe Saldana

from sklearn.metrics import pairwise_distances
from PIL import Image

# Function to calculate average embedding for each label
def calculate_average_embeddings(base_folder):
    average_embeddings = {}

    for label_folder in os.listdir(base_folder):
        label_path = os.path.join(base_folder, label_folder)
        if os.path.isdir(label_path):
            label_embeddings = []
            for file_name in os.listdir(label_path):
                file_path = os.path.join(label_path, file_name)
                # Load embeddings using your preferred method (e.g., np.load())
                embedding = np.load(file_path)
                label_embeddings.append(embedding)

            # Calculate average embedding for the label
            average_embedding = np.mean(label_embeddings, axis=0)
            average_embeddings[label_folder] = average_embedding

    return average_embeddings

# Function to recognize faces based on existing embeddings
def recognize_faces(test_folder, average_embeddings):
    predictions = []

    for label_folder in os.listdir(test_folder):
        label_path = os.path.join(test_folder, label_folder)
        if os.path.isdir(label_path):
            for file_name in os.listdir(label_path):
                file_path = os.path.join(label_path, file_name)

                # Load test embedding from the saved numpy file
                test_embedding = np.load(file_path)

                # Compare test embedding with averaged embeddings
                distances = {}
                for label, avg_embedding in average_embeddings.items():
                    distance = np.linalg.norm(test_embedding - avg_embedding)
                    distances[label] = distance

                # Predict the label with the minimum distance
                predicted_label = min(distances, key=distances.get)
                predictions.append((file_name, label_folder, predicted_label))

    return predictions

import random

# Function to display images with actual and predicted labels
def display_random_images(predictions, image_folder, num_images=40):
    selected_predictions = random.sample(predictions, min(num_images, len(predictions)))
    
    # Calculate the number of rows and columns based on the desired number of images
    num_rows = (num_images + 4) // 5  # Ensure at least 1 row
    num_cols = min(5, num_images)  # Maximum of 5 columns
    
    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 2 * num_rows))
    fig.subplots_adjust(hspace=0.5)  # Adjust the vertical spacing

    for i, (file_name, actual_label, predicted_label) in enumerate(selected_predictions):
        # Extract identifier from the file name
        identifier_start = file_name.find("_detected_face_") + len("_detected_face_")
        identifier_end = file_name.find("_", identifier_start)
        if identifier_end == -1:
            identifier = file_name[identifier_start:]
        else:
            identifier = file_name[identifier_start:identifier_end]

        # Construct the file path for the corresponding image
        image_name = f"detected_face_{identifier}.jpg"
        image_path = os.path.join(image_folder, actual_label, image_name)

        # Display the actual image along with labels
        try:
            image = Image.open(image_path)
            axes[i // 5, i % 5].imshow(image)
            axes[i // 5, i % 5].set_title(f"Actual: {actual_label}\nPredicted: {predicted_label}")
            axes[i // 5, i % 5].axis('off')

        except FileNotFoundError:
            print(f"Image not found for {identifier}. Skipping to the next one.")

    plt.show()

display_random_images(predictions, '/kaggle/working/train_detected_faces', num_images=40)

# folder paths
base_folder = 'working/extracted_embeddings'
test_folder = 'working/extracted_embeddingstest'
image_folder = 'working/train_detected_faces'

# Load existing average embeddings
average_embeddings = calculate_average_embeddings(base_folder)

# Recognize faces in the test folder
predictions = recognize_faces(test_folder, average_embeddings)

# Display 10 images with actual and predicted labels
#display_images(predictions, image_folder, num_images=10)

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# Display a sample of predictions
print(predictions[:10])

[('pins_Adriana Lima_detected_face_0_embedding.npy', 'pins_Adriana Lima', 'pins_Adriana Lima'), ('pins_Adriana Lima_detected_face_101_embedding.npy', 'pins_Adriana Lima', 'pins_Adriana Lima'), ('pins_Alex Lawther_detected_face_214_embedding.npy', 'pins_Alex Lawther', 'pins_Alex Lawther'), ('pins_Alex Lawther_detected_face_215_embedding.npy', 'pins_Alex Lawther', 'pins_Alex Lawther'), ('pins_Alexandra Daddario_detected_face_365_embedding.npy', 'pins_Alexandra Daddario', 'pins_Alexandra Daddario'), ('pins_Alexandra Daddario_detected_face_366_embedding.npy', 'pins_Alexandra Daddario', 'pins_Alexandra Daddario'), ('pins_Alvaro Morte_detected_face_590_embedding.npy', 'pins_Alvaro Morte', 'pins_Alvaro Morte'), ('pins_Alvaro Morte_detected_face_591_embedding.npy', 'pins_Alvaro Morte', 'pins_Alvaro Morte'), ('pins_alycia dabnem carey_detected_face_729_embedding.npy', 'pins_alycia dabnem carey', 'pins_alycia dabnem carey'), ('pins_alycia dabnem carey_detected_face_730_embedding.npy', 'pins_alycia dabnem carey', 'pins_alycia dabnem carey')]

def load_test_embeddings(test_folder):
    test_embeddings = []

    for label_folder in os.listdir(test_folder):
        label_path = os.path.join(test_folder, label_folder)
        if os.path.isdir(label_path):
            for file_name in os.listdir(label_path):
                file_path = os.path.join(label_path, file_name)

                # Load test embedding from the saved numpy file
                test_embedding = np.load(file_path)
                test_embeddings.append((file_name, label_folder, test_embedding))

    return test_embeddings


# folder paths
base_folder = 'working/extracted_embeddings'
test_folder = 'working/extracted_embeddingstest'

# Load existing average embeddings
average_embeddings = calculate_average_embeddings(base_folder)

# Load test embeddings
test_embeddings = load_test_embeddings(test_folder)

# Randomly pick two test embeddings
random_test_embeddings = random.sample(test_embeddings, 2)

# Print the randomly picked test embeddings
#for file_name, label, test_embedding in random_test_embeddings:
 #   print(f"Randomly Picked Test Embedding ({label}): {test_embedding}")

# Calculate and print distances between the random test embeddings and all average embeddings
for file_name, label, test_embedding in random_test_embeddings:
    print(f"\nDistances for {file_name} ({label}) against Average Embeddings:")
    for avg_label, avg_embedding in average_embeddings.items():
        distance = np.linalg.norm(test_embedding - avg_embedding)
        print(f"  Distance to {avg_label}: {distance}")

Distances for pins_Keanu Reeves_detected_face_8927_embedding.npy (pins_Keanu Reeves) against Average Embeddings:
  Distance to pins_Adriana Lima: 1.0907365083694458
  Distance to pins_Alex Lawther: 1.0727401971817017
  Distance to pins_Alexandra Daddario: 1.212906002998352
  Distance to pins_Alvaro Morte: 0.897905707359314
  Distance to pins_alycia dabnem carey: 1.0393484830856323
  Distance to pins_Amanda Crew: 1.0917949676513672
  Distance to pins_amber heard: 1.1675137281417847
  Distance to pins_Andy Samberg: 1.0244650840759277
  Distance to pins_Anne Hathaway: 1.239278793334961
  Distance to pins_Anthony Mackie: 1.282767415046692
  Distance to pins_Avril Lavigne: 1.2021830081939697
  Distance to pins_barack obama: 1.1130152940750122
  Distance to pins_barbara palvin: 1.106818675994873
  Distance to pins_Ben Affleck: 0.8154085874557495
  Distance to pins_Bill Gates: 1.2312308549880981
  Distance to pins_Bobby Morley: 0.8664924502372742
  Distance to pins_Brenton Thwaites: 0.902674674987793
  Distance to pins_Brian J. Smith: 0.934938371181488
  Distance to pins_Brie Larson: 1.137947916984558
  Distance to pins_camila mendes: 1.3246116638183594
  Distance to pins_Chris Evans: 0.8283305168151855
  Distance to pins_Chris Hemsworth: 0.8055009841918945
  Distance to pins_Chris Pratt: 0.7595720887184143
  Distance to pins_Christian Bale: 0.8515406847000122
  Distance to pins_Cristiano Ronaldo: 1.1392863988876343
  Distance to pins_Danielle Panabaker: 1.2027554512023926
  Distance to pins_Dominic Purcell: 0.9681774973869324
  Distance to pins_Dwayne Johnson: 1.0584139823913574
  Distance to pins_Eliza Taylor: 1.1737382411956787
  Distance to pins_Elizabeth Lail: 1.0739308595657349
  Distance to pins_elizabeth olsen: 1.1441128253936768
  Distance to pins_ellen page: 1.104724645614624
  Distance to pins_elon musk: 1.1403863430023193
  Distance to pins_Emilia Clarke: 1.0858677625656128
  Distance to pins_Emma Stone: 1.1308943033218384
  Distance to pins_Emma Watson: 1.2161176204681396
  Distance to pins_gal gadot: 1.1420031785964966
  Distance to pins_grant gustin: 0.9414496421813965
  Distance to pins_Gwyneth Paltrow: 1.0873854160308838
  Distance to pins_Henry Cavil: 0.8297377824783325
  Distance to pins_Hugh Jackman: 0.8966360688209534
  Distance to pins_Inbar Lavi: 1.2356116771697998
  Distance to pins_Irina Shayk: 1.1176707744598389
  Distance to pins_Jake Mcdorman: 0.9217404723167419
  Distance to pins_Jason Momoa: 0.7688860297203064
  Distance to pins_jeff bezos: 1.2059688568115234
  Distance to pins_Jennifer Lawrence: 1.1183065176010132
  Distance to pins_Jeremy Renner: 1.1273192167282104
  Distance to pins_Jessica Barden: 1.0695838928222656
  Distance to pins_Jimmy Fallon: 0.971354603767395
  Distance to pins_Johnny Depp: 0.9608635306358337
  Distance to pins_Josh Radnor: 0.9516727328300476
  Distance to pins_Katharine Mcphee: 1.1850477457046509
  Distance to pins_Katherine Langford: 1.2283146381378174
  Distance to pins_Keanu Reeves: 0.6936250329017639
  Distance to pins_kiernen shipka: 1.2321311235427856
  Distance to pins_Krysten Ritter: 1.3175181150436401
  Distance to pins_Leonardo DiCaprio: 1.0553410053253174
  Distance to pins_Lili Reinhart: 1.208667278289795
  Distance to pins_Lindsey Morgan: 1.1736204624176025
  Distance to pins_Lionel Messi: 0.8549008965492249
  Distance to pins_Logan Lerman: 1.162579894065857
  Distance to pins_Madelaine Petsch: 1.0973961353302002
  Distance to pins_Maisie Williams: 1.3274699449539185
  Distance to pins_margot robbie: 1.191126823425293
  Distance to pins_Maria Pedraza: 1.1738359928131104
  Distance to pins_Marie Avgeropoulos: 1.1432446241378784
  Distance to pins_Mark Ruffalo: 0.8989967703819275
  Distance to pins_Mark Zuckerberg: 1.1447561979293823
  Distance to pins_Megan Fox: 1.0787358283996582
  Distance to pins_melissa fumero: 1.2268697023391724
  Distance to pins_Miley Cyrus: 1.2401692867279053
  Distance to pins_Millie Bobby Brown: 1.1321072578430176
  Distance to pins_Morena Baccarin: 1.3090741634368896
  Distance to pins_Morgan Freeman: 1.2679976224899292
  Distance to pins_Nadia Hilker: 1.0966815948486328
  Distance to pins_Natalie Dormer: 1.2565295696258545
  Distance to pins_Natalie Portman: 1.2102175951004028
  Distance to pins_Neil Patrick Harris: 0.9708298444747925
  Distance to pins_Pedro Alonso: 1.0211684703826904
  Distance to pins_Penn Badgley: 0.8887699842453003
  Distance to pins_Rami Malek: 1.1131808757781982
  Distance to pins_Rebecca Ferguson: 1.1914513111114502
  Distance to pins_Richard Harmon: 0.9950404763221741
  Distance to pins_Rihanna: 1.1897852420806885
  Distance to pins_Robert De Niro: 0.8974473476409912
  Distance to pins_Robert Downey Jr: 0.933534562587738
  Distance to pins_Sarah Wayne Callies: 1.1564478874206543
  Distance to pins_scarlett johansson: 1.1990810632705688
  Distance to pins_Selena Gomez: 1.166921854019165
  Distance to pins_Shakira Isabel Mebarak: 1.2005335092544556
  Distance to pins_Sophie Turner: 1.2624390125274658
  Distance to pins_Stephen Amell: 0.829555094242096
  Distance to pins_Taylor Swift: 1.1382250785827637
  Distance to pins_Tom Cruise: 0.9140512347221375
  Distance to pins_tom ellis: 0.8193860054016113
  Distance to pins_Tom Hardy: 0.860024094581604
  Distance to pins_Tom Hiddleston: 0.9809429049491882
  Distance to pins_Tom Holland: 1.1067126989364624
  Distance to pins_Tuppence Middleton: 1.2489343881607056
  Distance to pins_Ursula Corbero: 1.0112590789794922
  Distance to pins_Wentworth Miller: 1.0710060596466064
  Distance to pins_Zac Efron: 1.0665520429611206
  Distance to pins_Zendaya: 1.0551358461380005
  Distance to pins_Zoe Saldana: 1.1619575023651123

Distances for pins_melissa fumero_detected_face_11687_embedding.npy (pins_melissa fumero) against Average Embeddings:
  Distance to pins_Adriana Lima: 1.0472183227539062
  Distance to pins_Alex Lawther: 1.1923283338546753
  Distance to pins_Alexandra Daddario: 1.0012543201446533
  Distance to pins_Alvaro Morte: 1.2829509973526
  Distance to pins_alycia dabnem carey: 1.0144710540771484
  Distance to pins_Amanda Crew: 0.9944965839385986
  Distance to pins_amber heard: 1.1784979104995728
  Distance to pins_Andy Samberg: 1.1759744882583618
  Distance to pins_Anne Hathaway: 1.0124280452728271
  Distance to pins_Anthony Mackie: 1.3192522525787354
  Distance to pins_Avril Lavigne: 1.0975881814956665
  Distance to pins_barack obama: 1.2571238279342651
  Distance to pins_barbara palvin: 0.9462469816207886
  Distance to pins_Ben Affleck: 1.201863408088684
  Distance to pins_Bill Gates: 1.3741244077682495
  Distance to pins_Bobby Morley: 1.2897197008132935
  Distance to pins_Brenton Thwaites: 1.4019566774368286
  Distance to pins_Brian J. Smith: 1.2988516092300415
  Distance to pins_Brie Larson: 0.93565833568573
  Distance to pins_camila mendes: 1.1032861471176147
  Distance to pins_Chris Evans: 1.260750651359558
  Distance to pins_Chris Hemsworth: 1.3757761716842651
  Distance to pins_Chris Pratt: 1.2881803512573242
  Distance to pins_Christian Bale: 1.3047698736190796
  Distance to pins_Cristiano Ronaldo: 1.3680692911148071
  Distance to pins_Danielle Panabaker: 0.8331988453865051
  Distance to pins_Dominic Purcell: 1.2457623481750488
  Distance to pins_Dwayne Johnson: 1.3852949142456055
  Distance to pins_Eliza Taylor: 1.0064679384231567
  Distance to pins_Elizabeth Lail: 1.0226492881774902
  Distance to pins_elizabeth olsen: 1.0181124210357666
  Distance to pins_ellen page: 0.9262386560440063
  Distance to pins_elon musk: 1.241856575012207
  Distance to pins_Emilia Clarke: 0.9397671818733215
  Distance to pins_Emma Stone: 0.9803264141082764
  Distance to pins_Emma Watson: 0.9570375680923462
  Distance to pins_gal gadot: 0.9657225012779236
  Distance to pins_grant gustin: 1.3564858436584473
  Distance to pins_Gwyneth Paltrow: 1.1665430068969727
  Distance to pins_Henry Cavil: 1.2245283126831055
  Distance to pins_Hugh Jackman: 1.2988572120666504
  Distance to pins_Inbar Lavi: 1.0178327560424805
  Distance to pins_Irina Shayk: 1.0671101808547974
  Distance to pins_Jake Mcdorman: 1.264137864112854
  Distance to pins_Jason Momoa: 1.3041166067123413
  Distance to pins_jeff bezos: 1.2952908277511597
  Distance to pins_Jennifer Lawrence: 1.0761152505874634
  Distance to pins_Jeremy Renner: 1.24166738986969
  Distance to pins_Jessica Barden: 0.9810200929641724
  Distance to pins_Jimmy Fallon: 1.233288288116455
  Distance to pins_Johnny Depp: 1.2020756006240845
  Distance to pins_Josh Radnor: 1.2048310041427612
  Distance to pins_Katharine Mcphee: 0.9875227808952332
  Distance to pins_Katherine Langford: 0.9426784515380859
  Distance to pins_Keanu Reeves: 1.2889806032180786
  Distance to pins_kiernen shipka: 0.9725674390792847
  Distance to pins_Krysten Ritter: 1.1046295166015625
  Distance to pins_Leonardo DiCaprio: 1.2437162399291992
  Distance to pins_Lili Reinhart: 1.0465792417526245
  Distance to pins_Lindsey Morgan: 0.9623363018035889
  Distance to pins_Lionel Messi: 1.3091732263565063
  Distance to pins_Logan Lerman: 1.3325268030166626
  Distance to pins_Madelaine Petsch: 0.9654428362846375
  Distance to pins_Maisie Williams: 1.0539387464523315
  Distance to pins_margot robbie: 1.0828676223754883
  Distance to pins_Maria Pedraza: 0.9950555562973022
  Distance to pins_Marie Avgeropoulos: 1.0161763429641724
  Distance to pins_Mark Ruffalo: 1.1699845790863037
  Distance to pins_Mark Zuckerberg: 1.284671664237976
  Distance to pins_Megan Fox: 0.9973228573799133
  Distance to pins_melissa fumero: 0.4341469705104828
  Distance to pins_Miley Cyrus: 1.0216658115386963
  Distance to pins_Millie Bobby Brown: 0.9451692700386047
  Distance to pins_Morena Baccarin: 1.0219240188598633
  Distance to pins_Morgan Freeman: 1.3772648572921753
  Distance to pins_Nadia Hilker: 1.1397740840911865
  Distance to pins_Natalie Dormer: 1.0432283878326416
  Distance to pins_Natalie Portman: 0.8651353120803833
  Distance to pins_Neil Patrick Harris: 1.293210506439209
  Distance to pins_Pedro Alonso: 1.1097618341445923
  Distance to pins_Penn Badgley: 1.2915738821029663
  Distance to pins_Rami Malek: 1.2150154113769531
  Distance to pins_Rebecca Ferguson: 0.9449279308319092
  Distance to pins_Richard Harmon: 1.245517611503601
  Distance to pins_Rihanna: 1.263484001159668
  Distance to pins_Robert De Niro: 1.2608880996704102
  Distance to pins_Robert Downey Jr: 1.2616602182388306
  Distance to pins_Sarah Wayne Callies: 0.8238881230354309
  Distance to pins_scarlett johansson: 1.100235104560852
  Distance to pins_Selena Gomez: 0.999914288520813
  Distance to pins_Shakira Isabel Mebarak: 1.1549603939056396
  Distance to pins_Sophie Turner: 0.975644588470459
  Distance to pins_Stephen Amell: 1.2820969820022583
  Distance to pins_Taylor Swift: 1.0795440673828125
  Distance to pins_Tom Cruise: 1.2875176668167114
  Distance to pins_tom ellis: 1.2337234020233154
  Distance to pins_Tom Hardy: 1.2114332914352417
  Distance to pins_Tom Hiddleston: 1.2515019178390503
  Distance to pins_Tom Holland: 1.2163454294204712
  Distance to pins_Tuppence Middleton: 0.8098502159118652
  Distance to pins_Ursula Corbero: 1.0663021802902222
  Distance to pins_Wentworth Miller: 1.3005812168121338
  Distance to pins_Zac Efron: 1.3336941003799438
  Distance to pins_Zendaya: 1.196130633354187
  Distance to pins_Zoe Saldana: 1.1714837551116943

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Disable CUDA
torch.cuda.is_available = lambda : False

# Set the device to CPU
device = torch.device("cpu")

# Load embeddings and labels
data_dir = "working/extracted_embeddings"
embeddings = []
labels = []

for label_folder in os.listdir(data_dir):
    label_path = os.path.join(data_dir, label_folder)
    if os.path.isdir(label_path):
        label = label_folder  # Assuming the folder names are labels
        embeddings_per_label = [] 

        for file_name in os.listdir(label_path):
            file_path = os.path.join(label_path, file_name)
            if file_name.endswith(".npy"):
                embedding = np.load(file_path)
                embeddings_per_label.append(embedding)
                labels.append(label)

        embeddings.append(embeddings_per_label)

# Flatten the embeddings list
embeddings = [item for sublist in embeddings for item in sublist]

# Convert data to PyTorch tensors
X = torch.tensor(embeddings, dtype=torch.float32)
labels = np.array(labels)

# Use LabelEncoder to encode string labels into integers
label_encoder = LabelEncoder()
y = torch.tensor(label_encoder.fit_transform(labels), dtype=torch.long)

# Split the data into 80% training, 10% validation, and 10% test
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.2, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)

# Define an updated model with more layers
class UpdatedModel(nn.Module):
    def __init__(self, input_size, num_classes):
        super(UpdatedModel, self).__init__()
        self.fc1 = nn.Linear(input_size, 256)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Linear(256, 128)
        self.relu2 = nn.ReLU()
        self.fc3 = nn.Linear(128, 64)
        self.relu3 = nn.ReLU()
        self.fc4 = nn.Linear(64, num_classes)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu1(x)
        x = self.fc2(x)
        x = self.relu2(x)
        x = self.fc3(x)
        x = self.relu3(x)
        x = self.fc4(x)
        return x

# Initialize the model
input_size = X_train.shape[1]
num_classes = len(set(y_train))
model = UpdatedModel(input_size, num_classes).to(device)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop
num_epochs = 20
batch_size = 32

for epoch in range(num_epochs):
    model.train()
    for i in range(0, len(X_train), batch_size):
        inputs = X_train[i:i+batch_size].to(device)
        labels = y_train[i:i+batch_size].to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    # Validation
    model.eval()
    with torch.no_grad():
        val_inputs = X_val.to(device)
        val_labels = y_val.to(device)

        val_outputs = model(val_inputs)
        val_loss = criterion(val_outputs, val_labels)

    print(f"Epoch {epoch+1}/{num_epochs}, Train Loss: {loss.item():.4f}, Validation Loss: {val_loss.item():.4f}")

# Evaluation on the test data
model.eval()
with torch.no_grad():
    test_inputs = X_test.to(device)
    test_labels = y_test.to(device)

    test_outputs = model(test_inputs)
    test_loss = criterion(test_outputs, test_labels)

# Decode the predicted labels using inverse_transform
predicted_labels = label_encoder.inverse_transform(torch.argmax(test_outputs, dim=1).cpu().numpy())

# Decode the true labels using inverse_transform
true_labels = label_encoder.inverse_transform(y_test.cpu().numpy())

# Display classification report and confusion matrix
print("Classification Report:")
print(classification_report(true_labels, predicted_labels))

#print("Confusion Matrix:")
#print(confusion_matrix(true_labels, predicted_labels))

C:\Users\Ifham.Khan\AppData\Local\Temp\ipykernel_7464\3096603719.py:39: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\utils\tensor_new.cpp:264.)
  X = torch.tensor(embeddings, dtype=torch.float32)

Epoch 1/20, Train Loss: 1.1412, Validation Loss: 1.3178
Epoch 2/20, Train Loss: 0.6614, Validation Loss: 0.7568
Epoch 3/20, Train Loss: 0.4601, Validation Loss: 0.5850
Epoch 4/20, Train Loss: 0.3778, Validation Loss: 0.4945
Epoch 5/20, Train Loss: 0.3405, Validation Loss: 0.4458
Epoch 6/20, Train Loss: 0.3273, Validation Loss: 0.4145
Epoch 7/20, Train Loss: 0.3195, Validation Loss: 0.3889
Epoch 8/20, Train Loss: 0.3022, Validation Loss: 0.3683
Epoch 9/20, Train Loss: 0.2806, Validation Loss: 0.3599
Epoch 10/20, Train Loss: 0.2503, Validation Loss: 0.3586
Epoch 11/20, Train Loss: 0.2537, Validation Loss: 0.3603
Epoch 12/20, Train Loss: 0.2762, Validation Loss: 0.3651
Epoch 13/20, Train Loss: 0.2950, Validation Loss: 0.3817
Epoch 14/20, Train Loss: 0.1758, Validation Loss: 0.4054
Epoch 15/20, Train Loss: 0.1757, Validation Loss: 0.4104
Epoch 16/20, Train Loss: 0.1522, Validation Loss: 0.3856
Epoch 17/20, Train Loss: 0.1891, Validation Loss: 0.3679
Epoch 18/20, Train Loss: 0.1751, Validation Loss: 0.3812
Epoch 19/20, Train Loss: 0.0899, Validation Loss: 0.4209
Epoch 20/20, Train Loss: 0.0527, Validation Loss: 0.4481
Classification Report:
                             precision    recall  f1-score   support

          pins_Adriana Lima       1.00      0.94      0.97        33
          pins_Alex Lawther       0.89      0.89      0.89        18
    pins_Alexandra Daddario       1.00      0.90      0.95        42
          pins_Alvaro Morte       0.77      0.94      0.85        18
           pins_Amanda Crew       1.00      0.95      0.98        22
          pins_Andy Samberg       0.94      0.81      0.87        36
         pins_Anne Hathaway       0.83      0.97      0.90        40
        pins_Anthony Mackie       1.00      1.00      1.00        21
         pins_Avril Lavigne       1.00      0.95      0.98        21
           pins_Ben Affleck       0.82      0.90      0.86        20
            pins_Bill Gates       1.00      1.00      1.00        15
          pins_Bobby Morley       0.62      0.67      0.65        15
      pins_Brenton Thwaites       0.95      0.93      0.94        41
        pins_Brian J. Smith       0.94      0.94      0.94        16
           pins_Brie Larson       0.69      0.87      0.77        23
           pins_Chris Evans       0.95      0.77      0.85        26
       pins_Chris Hemsworth       0.89      0.89      0.89        27
           pins_Chris Pratt       1.00      0.93      0.96        40
        pins_Christian Bale       1.00      0.85      0.92        20
     pins_Cristiano Ronaldo       1.00      0.91      0.95        11
    pins_Danielle Panabaker       1.00      0.97      0.99        34
       pins_Dominic Purcell       0.96      1.00      0.98        25
        pins_Dwayne Johnson       1.00      0.78      0.88         9
          pins_Eliza Taylor       0.77      1.00      0.87        24
        pins_Elizabeth Lail       0.86      0.63      0.73        19
         pins_Emilia Clarke       0.82      0.97      0.89        34
            pins_Emma Stone       1.00      0.88      0.93        16
           pins_Emma Watson       0.97      0.97      0.97        37
       pins_Gwyneth Paltrow       0.96      0.92      0.94        26
           pins_Henry Cavil       1.00      0.65      0.79        20
          pins_Hugh Jackman       1.00      0.97      0.98        30
            pins_Inbar Lavi       1.00      0.86      0.93        22
           pins_Irina Shayk       0.89      1.00      0.94        17
         pins_Jake Mcdorman       1.00      1.00      1.00        26
           pins_Jason Momoa       0.96      0.96      0.96        24
     pins_Jennifer Lawrence       1.00      0.96      0.98        23
         pins_Jeremy Renner       1.00      1.00      1.00        30
        pins_Jessica Barden       1.00      0.76      0.86        29
          pins_Jimmy Fallon       0.95      0.73      0.83        26
           pins_Johnny Depp       0.97      0.94      0.95        31
           pins_Josh Radnor       0.73      0.92      0.81        12
      pins_Katharine Mcphee       0.96      0.96      0.96        25
    pins_Katherine Langford       0.95      0.86      0.90        42
          pins_Keanu Reeves       0.92      0.92      0.92        25
        pins_Krysten Ritter       1.00      0.87      0.93        30
     pins_Leonardo DiCaprio       0.89      0.89      0.89        44
         pins_Lili Reinhart       0.79      0.95      0.86        20
        pins_Lindsey Morgan       0.84      0.84      0.84        25
          pins_Lionel Messi       1.00      0.85      0.92        13
          pins_Logan Lerman       0.93      0.97      0.95        39
      pins_Madelaine Petsch       0.97      0.97      0.97        31
       pins_Maisie Williams       0.92      0.96      0.94        24
         pins_Maria Pedraza       0.83      0.71      0.77        14
    pins_Marie Avgeropoulos       0.96      1.00      0.98        23
          pins_Mark Ruffalo       0.63      1.00      0.77        24
       pins_Mark Zuckerberg       0.93      0.93      0.93        15
             pins_Megan Fox       1.00      0.85      0.92        34
           pins_Miley Cyrus       0.89      0.94      0.91        17
    pins_Millie Bobby Brown       0.89      0.83      0.86        30
       pins_Morena Baccarin       0.90      1.00      0.95        27
        pins_Morgan Freeman       0.83      0.83      0.83         6
          pins_Nadia Hilker       0.88      0.93      0.90        15
        pins_Natalie Dormer       0.97      0.97      0.97        32
       pins_Natalie Portman       1.00      0.90      0.95        20
   pins_Neil Patrick Harris       0.88      0.88      0.88        16
          pins_Pedro Alonso       0.94      1.00      0.97        15
          pins_Penn Badgley       0.97      0.97      0.97        30
            pins_Rami Malek       0.94      0.91      0.93        35
      pins_Rebecca Ferguson       0.89      0.91      0.90        44
        pins_Richard Harmon       0.75      0.90      0.82        20
               pins_Rihanna       0.88      0.94      0.91        16
        pins_Robert De Niro       0.60      0.89      0.72        27
      pins_Robert Downey Jr       0.83      0.93      0.88        43
   pins_Sarah Wayne Callies       0.83      0.86      0.84        22
          pins_Selena Gomez       0.86      0.86      0.86        22
pins_Shakira Isabel Mebarak       0.96      0.90      0.93        29
         pins_Sophie Turner       0.97      0.94      0.95        32
         pins_Stephen Amell       1.00      0.92      0.96        36
          pins_Taylor Swift       1.00      1.00      1.00         9
            pins_Tom Cruise       0.93      0.97      0.95        29
             pins_Tom Hardy       0.81      0.85      0.83        20
        pins_Tom Hiddleston       0.96      0.79      0.86        28
           pins_Tom Holland       0.95      0.93      0.94        42
    pins_Tuppence Middleton       1.00      1.00      1.00        15
        pins_Ursula Corbero       1.00      0.90      0.95        21
      pins_Wentworth Miller       0.81      0.96      0.88        26
             pins_Zac Efron       0.97      1.00      0.98        30
               pins_Zendaya       0.88      0.95      0.91        22
           pins_Zoe Saldana       1.00      0.97      0.99        35
   pins_alycia dabnem carey       0.85      0.97      0.91        30
           pins_amber heard       0.96      0.93      0.94        27
          pins_barack obama       0.92      1.00      0.96        12
        pins_barbara palvin       0.87      0.97      0.92        35
         pins_camila mendes       0.86      0.95      0.90        20
       pins_elizabeth olsen       0.97      0.93      0.95        40
            pins_ellen page       0.97      0.97      0.97        29
             pins_elon musk       0.95      0.90      0.92        20
             pins_gal gadot       0.92      0.88      0.90        25
          pins_grant gustin       1.00      0.96      0.98        24
            pins_jeff bezos       1.00      1.00      1.00         8
        pins_kiernen shipka       1.00      0.96      0.98        28
         pins_margot robbie       1.00      0.88      0.94        25
        pins_melissa fumero       1.00      0.93      0.96        27
    pins_scarlett johansson       0.95      0.95      0.95        38
             pins_tom ellis       0.96      0.92      0.94        26

                   accuracy                           0.92      2672
                  macro avg       0.92      0.91      0.91      2672
               weighted avg       0.93      0.92      0.92      2672

#pip install facenet-pytorch opencv-python matplotlib

import os
import random
from facenet_pytorch import InceptionResnetV1, MTCNN
from PIL import Image
import matplotlib.pyplot as plt
import cv2
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
import torch

# Function to load actor embeddings and names
def load_actor_data(embeddings_directory):
    actor_embeddings = []
    actor_names = []

    for actor_folder in os.listdir(embeddings_directory):
        actor_folder_path = os.path.join(embeddings_directory, actor_folder)

        # Assuming each actor's folder contains files like "pins_ActorName_detected_face_*.npy"
        for embedding_file in os.listdir(actor_folder_path):
            embedding_file_path = os.path.join(actor_folder_path, embedding_file)

            # Load the embedding
            embedding = np.load(embedding_file_path)

            # Append the embedding and actor name
            actor_embeddings.append(embedding)
            actor_names.append(actor_folder)

    return np.array(actor_embeddings), np.array(actor_names)

# Function to process images and calculate embeddings
def process_images(frames_directory, face_embedder, mtcnn, actor_embeddings, actor_names):
    # List all files in the frames directory
    frame_files = os.listdir(frames_directory)

    # Pick two random files from the frames directory
    face1_file, face2_file = np.random.choice(frame_files, 2, replace=False)

    # Read the face images
    face1_path = os.path.join(frames_directory, face1_file)
    face2_path = os.path.join(frames_directory, face2_file)

    face1 = cv2.imread(face1_path)
    face1_rgb = cv2.cvtColor(face1, cv2.COLOR_BGR2RGB)

    face2 = cv2.imread(face2_path)
    face2_rgb = cv2.cvtColor(face2, cv2.COLOR_BGR2RGB)

    # Perform face detection using MTCNN
    boxes1, probs1 = mtcnn.detect(face1_rgb)
    boxes2, probs2 = mtcnn.detect(face2_rgb)

    if boxes1 is not None and boxes2 is not None:
        # Extract faces using the bounding boxes
        face1_box = boxes1[0].astype(int)
        face1_rgb = face1_rgb[face1_box[1]:face1_box[3], face1_box[0]:face1_box[2]]

        face2_box = boxes2[0].astype(int)
        face2_rgb = face2_rgb[face2_box[1]:face2_box[3], face2_box[0]:face2_box[2]]

        # Display original face images
        plt.subplot(1, 2, 1)
        plt.imshow(Image.fromarray(face1_rgb))
        plt.title(f"Original Face 1\nFrame: {face1_file}")
        plt.axis('off')

        plt.subplot(1, 2, 2)
        plt.imshow(Image.fromarray(face2_rgb))
        plt.title(f"Original Face 2\nFrame: {face2_file}")
        plt.axis('off')

        plt.show()

        # Convert NumPy arrays to PyTorch tensors
        face1_tensor = torch.from_numpy(face1_rgb.transpose(2, 0, 1)).unsqueeze(0).float() / 255.0
        face2_tensor = torch.from_numpy(face2_rgb.transpose(2, 0, 1)).unsqueeze(0).float() / 255.0

        # Calculate embeddings for the faces
        embeddings1 = face_embedder(face1_tensor).detach().numpy()
        embeddings2 = face_embedder(face2_tensor).detach().numpy()

        # Calculate distance between the two embeddings
        distance = np.linalg.norm(embeddings1 - embeddings2)

        # Find the actor with the closest embedding
        min_distance_index = np.argmin(euclidean_distances(embeddings1, actor_embeddings))
        actor_name = actor_names[min_distance_index]

        # Display the distance between the two embeddings
        print(f"Distance between embeddings: {distance:.4f}")
        print(f"Predicted actor: {actor_name}")
    else:
        print("Face not detected in one or both of the images.")

# Directory paths
frames_directory = 'C:\\Users\\Ifham.Khan\\Desktop\\Final Year Project\\105_classes_pins_dataset\\pins_Emma Watson'
embeddings_directory = 'working/extracted_embeddings'

# Initialize MTCNN for face detection
mtcnn = MTCNN(keep_all=True)

# Initialize InceptionResnetV1 for face embedding
face_embedder = InceptionResnetV1(pretrained='vggface2').eval()

# Load actor embeddings and names
actor_embeddings, actor_names = load_actor_data(embeddings_directory)

# Process face images, calculate embeddings, and display results
process_images(frames_directory, face_embedder, mtcnn, actor_embeddings, actor_names)

Distance between embeddings: 1.0611
Predicted actor: pins_Emma Watson

import os

# Define the directory path
save_directory = r'C:\Users\Ifham.Khan\Desktop\Final Year Project'

# Define the file name
file_name = 'Face_detection.pth'

# Combine directory path and file name
model_save_path = os.path.join(save_directory, file_name)

# Save the model's architecture and parameters
torch.save(model.state_dict(), model_save_path)

print(f"Model saved at {model_save_path}")

Model saved at C:\Users\Ifham.Khan\Desktop\Final Year Project\Face_detection.pth